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SPSI:A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery

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Plant Phenomics 2023年第3期5卷 642-656页

作者： Yapeng Wu Wenhui Wang Yangyang Gu Hengbiao Zheng Xia Yao Yan Zhu Weixing Cao Tao Cheng National Engineering and Technology Center for Information Agriculture(NETCIA) MARA Key Laboratory for Crop System Analysis and Decision MakingMOE Engineering Research Center of Smart AgricultureJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityOne WeigangNanjingJiangsu 210095PR China Langfang Normal University 100 Aimin West RoadLangfangHebei 065000PR China

Rapid and accurate estimation of panicle number per unit ground area(PNPA)in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final *** accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were *** study proposed a spectral-textural PNPA sensitive index(SPSI)from unmanned aerial vehicle(UAV)multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before *** effect of background materials on PNPA estimated by textural indices(TIs)was examined,and the composite index SPSI was constructed by integrating the optimal spectral index(SI)and ***,the performance of SPSI was evaluated in comparison with other indices(SI and TIs).The results demonstrated that green-pixel TIs yielded better performances than all-pixel TIs apart from TI_([HOM]),TI_([ENT]),and TI_([SEM])among all indices from 8 types of textural ***,which was calculated by the formula DATT_([850,730,675])+NDTICOR_([850,730]),exhibited the highest overall accuracies for any date in any dataset in comparison with DATT_([850,730,675]),TINDRE_([MEA]),and NDTICOR_([850,730]).For the unified models assembling 2 experimental datasets,the RV^(2) values of SPSI increased by 0.11 to 0.23,and both RMSE and RRMSE decreased by 16.43%to 38.79%as compared to the suboptimal index on each *** findings indicated that the SPSI is valuable in reducing the spectral saturation and has great potential to better estimate PNPA using high-resolution satellite imagery.

关键词： heading Wheat indices

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Lag Synchronization and Lag H∞ Synchronization for Multiweighted Coupled Reaction-Diffusion Neural Networks Suffering Topology Attacks

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IEEE Transactions on Control of Network systems 2025年第2期12卷 1488-1499页

作者： Wang, Jin-Liang Zhao, Xin-Yu Ren, Shun-Yan Huang, Tingwen Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Tiangong University School of Electronics & Information Engineering Tianjin300387 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper mainly discusses two kinds of coupled reaction-diffusion neural networks (CRNN) under topology attacks, that is, the cases with multistate couplings and with multiple spatial-diffusion couplings. On one hand, by adopting state feedback control scheme, the problem of the lag synchronization for the multistate coupled CRNN is addressed. Moreover, the influences of external disturbances for the lag synchronization of the CRNN with multistate couplings are also taken into consideration. On the other hand, several lag and lag H∞ synchronization conditions for CRNN with multiple spatial-diffusion couplings are derived under the help of the Kronecker product and the state feedback control method. Finally, two numerical examples are provided to verify the correctness of the derived criteria. © 2014 IEEE.

关键词： State feedback

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rafPS:A shapley-based visual analytics approach to interpret traffic

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Computational Visual Media 2024年第6期10卷 1101-1119页

作者： Zezheng Feng Yifan Jiang Hongjun Wang Zipei Fan Yuxin Ma Shuang-Hua Yang Huamin Qu Xuan Song Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 18055China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science University of ReadingBerkshire RG66AHUK Center for Spatial Information Science the University of TokyoTokyo 113-00331Japan Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic *** predictions are beneficial for understanding the situation and making traffic control ***,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end *** previous studies attempted to“open the black box”and increase the interpretability of generated ***,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain *** overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban *** measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different *** on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow *** real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

关键词： data visualization model interpretation urban planning urban visual analytics

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Facile integration of an Al-rich Al_(1-x)In_(x)N photodetector on free-standing GaN by radio-frequency magnetron sputtering

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Chinese Physics B 2023年第11期32卷 591-597页

作者：刘新科林之晨林钰恒陈建金邹苹周杰李博沈龙海朱德亮刘强俞文杰黎晓华顾泓王新中黄双武 College of Materials Science and Engineering Institute of Microelectronics(IME)Guangdong Research Center for Interfacial Engineering of Functional MaterialsShenzhen UniversityShenzhen 518060China School of Science Shenyang Ligong UniversityShenyang 110159China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Gusu Laboratory of Materials Science Suzhou 215123China Information Technology Research Institute Shenzhen Institute of Information TechnologyShenzhen 518172China

Al_(1-x)In_(x)N, a Ⅲ-nitride semiconductor material, is currently of great research interest due to its remarkable physical properties and chemical stability. When the Al and In compositions are tuned, its band-gap energy varies from 0.7 eV to 6.2 eV, which shows great potential for application in photodetectors. Here, we report the fabrication and performance evaluation of integrated Al_(1-x)In_(x)N on a free-standing GaN substrate through direct radio-frequency magnetron *** optical properties of Al_(1-x)In_(x)N will be enhanced by the polarization effect of a heterostructure composed of Al_(1-x)In_(x)N and other Ⅲ-nitride materials. An Al_(1-x)In_(x)N/Ga N visible-light photodetector was prepared by semiconductor fabrication technologies such as lithography and metal deposition. The highest photoresponsivity achieved was 1.52 A·W^(-1)under 365 nm wavelength illumination and the photodetector was determined to have the composition Al0.75In0.25N/GaN.A rise time of 0.55 s was observed after transient analysis of the device. The prepared Al_(1-x)In_(x)N visible-light photodetector had a low dark current, high photoresponsivity and fast response speed. By promoting a low-cost, simple fabrication method,this study expands the application of ternary alloy Al_(1-x)In_(x)N visible-light photodetectors in optical communication.

关键词： Ali-xIn N photodetector GaN radio-frequency magnetron sputtering ternary alloy

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A Multi-Angle MIMO-SAR Fusion Imaging Method Based on Improved Range Migration Algorithm and Improved Geographic information Scale Invariant Feature Transform Algorithm

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13938-13949页

作者： Zhou, Fang Jiao, Zhikuan Shen, Guoqing Xing, Mengdao Gashinova, Marina Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China University of Birmingham School of Engineering United Kingdom Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

In order to resolve the conflict between high pulse repetition frequency (PRF) and wide-swath imaging in multi-angle synthetic aperture radar (SAR), a multi-angle imaging model combined with multiple-input multiple-output synthetic aperture radar (MIMO-SAR) is proposed. The model utilizes more freedom in azimuth degrees provided by MIMO-SAR to remove azimuth ambiguity caused by low PRF, and successfully solves that contradiction. At the same time, considering the problem of low utilization of spectrum in multi-angle imaging SAR, an improved range migration algorithm (ImRMA) is proposed, which can effectively increase the utilization of the spectrum for data processing. Finally, an improved method of geographic information-scale invariant feature transform (ImGI-SIFT) is used to achieve the efficiency fusion of multi-angle imaging results. Simulation and real data experiments show that the effectiveness and superiority of the proposed imaging method, and the precision of the proposed fusion method. © 2008-2012 IEEE.

关键词： Data reduction

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Broadband and wide-angle plane focal surface Luneburg lens

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Chinese Physics B 2023年第6期32卷 16-20页

作者：李珏周杨阳陈焕阳 Institute of Electromagnetics and Acoustics and Department of Physics College of Physical Science and TechnologyXiamen UniversityXiamen 361005China Fujian Engineering Research Center for EDA Fujian Provincial Key Laboratory of Electromagnetic Wave Science and Detection TechnologyXiamen Key Laboratory of Multiphysics Electronic InformationXiamen UniversityXiamen 361005China

The energy crisis has aroused widespread concern, and the reform of energy structure is imminent. In the future,the energy structure will be dominated by the solar energy and other renewable energy sources. The solar concentrating technology as a promising method has been widely studied for collecting solar energy. However, the previous solar concentrating technologies suffer from some drawbacks, such as low focusing efficiency and large concentrating size. The Luneburg lens with highly efficient aberration-free focusing provides a new route for solar/energy concentrator. In this work, we designed a plane focal surface Luneburg lens(PFSLL) by transformation optics(TO). The PFSLL provides a relatively high focusing efficiency and concentration ratio of collection of energy. At the same time, it circumvents the disadvantage of curve surface of the classical Luneburg lens in device integration. Based on the reciprocity of electromagnetic waves, the PFSLL can also be applied to the antenna field to achieve broadband wide-angle scanning and highly directional radiation.

关键词： plane focal surface Luneburg lens(PFSLL) focusing efficiency concentration ratio of collection of energy

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First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data

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science China Earth sciences 2024年第10期67卷 3252-3269页

作者： Weiwei SUN Gang YANG Yuling HUANG Dehua MAO Ke HUANG Lin ZHU Xiangchao MENG Tian FENG Chao CHEN Yong GE Department of Geography and Spatial Information Techniques Ningbo UniversityNingbo 315211China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences&Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Natural Resources and Planning Bureau Shanghai Pudong New DistrictShanghai 200100China Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China School of Geography Science and Geomatics Engineering Suzhou University of Science and TechnologySuzhou 215009China

Wetland degradation has been accelerating in recent years globally. Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management. Despite being the world′s fourth largest continent, South America has limited research on wetland mapping, and there is currently no available map that provides comprehensive information on wetland distribution and categories in the region. To address this issue, we used Sentinel-1, Sentinel-2 and SRTM data, developed a sample collection method and a wetland mapping method with a collection of multi-source features such as optical features, polarization features and shape features for South American wetlands. We produced a 10-m resolution wetland map based on the Google Earth Engine(GEE) platform. Our Level-1 wetland cover map accurately captured six wetland sub-categories with an overall accuracy of 96.24% and a kappa coefficient of 0.8649, while our Level-2 water cover map included five sub-categories with an overall accuracy of 97.23% and a kappa coefficient of0.9368. The results show that the total area of existing wetlands in South America is approximately 1,737,000 km~2, which is6.8% of the total land area. Among the ten wetland categories, shallow sea had the largest area(960,527.4 km~2), while aquaculture ponds had the smallest area 1513.6 km~2. Swamp had the second largest area(306,240.1 km~2). Brazil, Argentina,Venezuela, Bolivia, and Colombia were found to have the largest wetland areas, with Brazil and Colombia having the most diverse wetland categories. This product can serve as baseline data for subsequent monitoring, management, and conservation of South American wetlands.

关键词： Wetland mapping Google Earth Engine Sentinel imagery South America

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Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1830-1844页

作者： Gao, Yulan Ye, Ziqiang Xiao, Yue Xiao, Ming Xiang, Wei University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China KTH Royal Institute of Technology Division of Information Science and Engineering Stockholm100 44 Sweden La Trobe University School of Engineering and Mathematical Sciences MelbourneVIC3086 Australia

Addressing data privacy concerns, Federated Learning (FL) has been recognized for its ability to train parameters locally on resource-constrained clients in a distributed manner. However, the problem of optimization of FL client selection and resource allocation in hierarchical Internet of Things (HieIoT) networks, where clients move in and out of each others’ D2D communication coverage and no FL server knows all the data owners, remains open. To bridge this gap, we propose a learner referral aided federated client selection (LRef-FedCS) approach, complemented by communications and computing resource scheduling, along with local model accuracy optimization (LMAO). LRef-FedCS enhances cost efficiency and FL model quality by enabling data owners to share FL task details within their trusted local networks, increasing the opportunity of the FL server choosing the optimal clients. Using Lyapunov optimization, the problem is transformed into a joint optimization problem (JOP). To address the JOP’s complexities, we combine a centralized method for LRef-FedCS and the self-adaptive global best harmony search algorithm for LMAO. For enhance scalability, a distributed LRef-FedCS based on a matching game is proposed. Numerical experiments on the Fashion-MNIST dataset show LRef-FedCS outperforms existing state-of-the-art approaches, delivering enhanced model accuracy with notable cost savings. © 2015 IEEE.

关键词： Federated learning

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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CC-Net: Consistency Learning Combined with Contrastive Learning for Kidney Ultrasound Segmentation

CC-Net: Consistency Learning Combined with Contrastive Learn...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Luo, Yang Liu, Jun Ding, Mengqian School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9781665410205

Due to the scarcity of medical images with labels and the fact that medical image data is not easily accessible. Kidney ultrasound (KUS) images have speckle noise, low image quality, acoustic shadows, and other reasons, which pose significant challenges to the kidney ultrasound image segmentation task. To solve the above two problems, a novel consistency learning and contrastive learning-based kidney ultrasound image segmentation method (CC-Net) has been proposed. Specifically, the student-teacher model was adopted as the semi-supervised learning framework and uncertainty estimation was introduced. The Consensus Uncertainty Map (CUM) strategy was proposed to generate segmentation results for the teacher model when calculating uncertainty. To solve the problems of acoustic shadows and low image quality in kidney ultrasound images, a kidney ultrasound image data augmentation method (KUS-AUG) and a convolutional block (KUS-CONV) tailored to the characteristics of kidney ultrasound images were proposed. In addition, contrastive learning was implemented to enhance the encoder's ability to learn more semantic information, due to its inherent advantage in learning data distribution. Finally, a series of experiments were conducted on the ultrasound image dataset and the method achieved the best performance. © 2024 IEEE.

关键词： Contrastive Learning

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