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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xinling Li Qingyan Wang Junping Zhang Yujing Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The remote sensing satellite system has steadily developed, and the availability of massive high-quality satellite remote sensing data has rapidly improved. The dynamic monitoring of land cover changes using hyperspectral data has received great attention. The existing change detection methods usually use the spatial correlation or spatial-spectral correlation of hyperspectral images, lacking an overall consideration of the three-dimensional temporal-spatial-spectral joint features, resulting in suboptimal change detection results. Given the considerations above, this paper proposes a SimAM multi-scale joint feature extraction network for hyperspectral image change detection. The proposed method first adopts SimAM multi-scale joint features network to consider the hyperspectral image as a whole, and extracts multi-scale temporal-spatial-spectral joint features from the network. Then, uses multi-scale feature weighted fusion module to weight different scale features after simple fusion, highlighting the change regions. Finally, adopts the batch-balance measurement module to measure the similarity of the bi-temporal fusion features and output the change detection result map. Experiments show that on two public datasets, the proposed method can achieve a good change detection effect.

关键词： Satellites Correlation Fuses Land surface Euclidean distance Feature extraction Monitoring

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Hyperspectral Image Classification Method Based On Node Similarity Feature Fusion

Hyperspectral Image Classification Method Based On Node Simi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiameng Wang Qingyan Wang Junping Zhang Yujing Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

There is abundant spectral and spatial information in Hyperspectral images (HSI). However, there exists a limitation of not using spatial information sufficiently in HSI classification. Besides, there is the limitation of mononuclear in feature extraction, resulting in insufficient feature extraction and insufficient utilization of data information. In view of these problems, a node similarity semi-supervised classification method of multiscale feature is proposed to break the limitation of mononuclear and achieve full extraction of spatial information. First, to extract pixel-level features, a three-dimensional (3-D) multiscale convolutional neural network (CNN) is used. Second, based on node similarity superpixel graph U-Net (NSGUNet) is proposed to extract superpixel-level features. Finally, the above two features are weighted fusing, the fused features are classified by sparse graph regularization. Experiments on three datasets illustrate that the proposed method is effective.

关键词： Accuracy Geoscience and remote sensing Feature extraction Convolutional neural networks Data mining Hyperspectral imaging Image classification

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Joint Classification Of Hyperspectral And LiDAR Data Based On Heterogeneous Attention Feature Fusion Network

Joint Classification Of Hyperspectral And LiDAR Data Based O...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zhaoyang Wang Qingyan Wang Junping Zhang Xintao Liang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The fusion of hyperspectral image (HSI) and LiDAR data for classification has gained widespread attention. However, the current fusion methods still have limitations on the use of heterogeneous data information and the interaction of heterogeneous data features. Therefore, we propose a classification method based on heterogeneous attention feature fusion network (HAFF-Net). Firstly, The convolutional neural network is employed to capture local spatial features from multi-scale inputs of heterogeneous data. Secondly, the heterogeneous feature attention module is designed to deeply fuse the extracted local spatial features and promote the interaction of heterogeneous data. Next, a transformer encoder is utilized to extract global spectral features. Finally, the two extracted features are classified by decision fusion using different classifiers. The proposed approach exhibits effectiveness through experimental results on two datasets.

关键词： Laser radar Fuses Convolution Soft sensors Geoscience and remote sensing Feature extraction Transformers

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A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

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Journal of Bionic engineering 2022年第1期19卷 224-239页

作者： Sen Tian Jin Zhang Xuanyu Shu Lingyu Chen Xin Niu You Wang School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China College of Information Science and Engineering Hunan Normal UniversityChangsha410081China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou310058China Science and Technology on Parallel and Distributed Laboratory College of ComputerNational University of Defense TechnologyChangsha410199China Key Laboratory of Industrial Control Technology Institute of Cyber Systems and ControlZhejiang UniversityHangzhou310027China

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and ***,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive ***,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the ***,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed *** analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in ***,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic ***,the network model is quantitatively analyzed by evaluation indexes from the perspective of *** experimental results show that the DBN network,LeNet5 network,and BP network have synchronous *** the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural *** KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic *** with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.

关键词： Artificial neural network(ANN) Back Propagation(BP)network Deep Belief Network(DBN) LeNet5 network Olfactory bionic model(KIII model) Small world Chaos Synchronous

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Blockchain-Assisted Electronic Medical Data-Sharing:Developments,Approaches and Perspectives

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Computers, Materials & Continua 2024年第12期81卷 3421-3450页

作者： Chenquan Gan Xinghai Xiao Qingyi Zhu Deepak Kumar Jain Akanksha Saini School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Artificial IntelligenceDalian University of TechnologyDalian116024China Symbiosis Institute of Technology Symbiosis International UniversityPune412115India College of Business and Law RMIT UniversityMelbourneVIC 3000Australia

Medical blockchain data-sharing is a technique that employs blockchain technology to facilitate the sharing of electronic medical *** blockchain is a decentralized digital ledger that ensures data-sharing security,transparency,and traceability through cryptographic technology and consensus ***,medical blockchain data-sharing methods have garnered significant attention and research ***,current methods have different storage and transmission measures for original data in the medical blockchain,resulting in large differences in performance and ***,we divide the medical blockchain data-sharing method into on-chain sharing and off-chain sharing according to the original data storage *** them,off-chain sharing can be subdivided into on-cloud sharing and local sharing according to whether the data is ***,we provide a detailed analysis of basic processes and research content for each ***,we summarize the challenges posed by the current methods and discuss future research directions.

关键词： Electronic medical data data-sharing medical blockchain cloud federated learning

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Reverse Time Migration of Ultra-Wideband Ground-Penetrating Radar Based on Multiple Time-Gating Algorithm

Reverse Time Migration of Ultra-Wideband Ground-Penetrating ...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Wu, Junjie Li, Haixiao Zhou, Yuanguo Cao, Le Wang, Yan Xi'an University of Science and Technology College of Communication and Information Engineering Xi'an China Xi'an University of Science and Technology College of Electrical and Control Engineering Xi'an China Graduate School of Chinese Aeronautical Establishment Yangzhou China Aviation Key Laboratory of Science and Technology on Electromagnetic Environment Effects Shenyang China National Key Laboratory of Electromagnetic Information Control and Effects Shenyang China

ISBN: (纸本)9798350355581

To address the issue of clutter interference in ground-penetrating radar (GPR) imaging using a VNA (Vector Network Analyzer), this paper improves the traditional reverse time migration (RTM) technique for ultra-wideband GPR imaging. The improved method retains the effective signals in the detection wavefield as the valid imaging area and suppresses signals in non-valid areas. Due to variations in the distance between the transmitting and receiving antennas and the number of anomalies in the experiment, the traditional time window cross-correlation is not ideal for this scenario. The improved method automatically applies reasonable windowing to each data channel, avoiding the simultaneous enhancement of clutter interference and effective signals through direct stacking, and preventing the filtering out of effective signals. Experimental results demonstrate that this method, when applied to real measured data and compared with the traditional cross-correlation RTM method, shows advantages in more accurately locating anomalous areas and suppressing clutter interference. This validates that the Multiple Time-Gating Reverse Time Migration (MTG-RTM) method offers better localization and higher imaging accuracy in practical engineering applications. © 2024 IEEE.

关键词： Receiving antennas

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Frequency Domain Separation of DAS Multi-Source Aliased Signals

Frequency Domain Separation of DAS Multi-Source Aliased Sign...

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2023 Asia communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Gu, Huaxin Liu, Shuaiqi Yu, Feihong Xu, Deyu Chen, Xingwei Shao, Liyang Wu, Yu Zhang, Haifeng College of Innovation and Entrepreneurship Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen China School of Information and Communication Engineering University of Electronic Science and Technology Chengdu China Research Institute of Tsinghua University Advanced Measurement and Control and Equipment R&d Center Chengdu China

ISBN: (纸本)9798350312614

In this paper, fast independent vector analysis (FastIVA) based on convolutional aliasing model is proposed to separate the aliased signals collected by distributed acoustic sensing (DAS) system, and the time-frequency entropy is used to judge the separation performance. The results show that the time-frequency entropy interval of the separated signals and the source signals correspond to each other, which means that the aliased signals are separated. This method is used to improve the accuracy of DAS system signal detection and recognition in complex real environment, and reduce the number of false alarm events. © 2023 IEEE.

关键词： convolutional aliasing model DAS FastIVA time-frequency entropy

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Acceleration of Formation control Based on Hessian Matrix of Artificial Potential Field 42

Acceleration of Formation Control Based on Hessian Matrix of...

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42nd Chinese control Conference, CCC 2023

作者： Han, Haoran Xi, Zhilong Lv, Maolong Cheng, Jian School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Air Traffic Control and Navigation College Air Force Engineering University Xian710051 China

ISBN: (纸本)9789887581543

The formation control of multi-agent system has become a hotspot in recent years. One of the most commonly used methods is to use artificial potential field (APF) to generate the attractive and repulsive force to organize the agents into a low-energy and stable formation. In this paper, we regard the APF as the difference in the desired position, and introduce the derivative of the gradient of the APF with respect to time, which is simplified as the Hessian matrix of the APF, into the formation control from the perspective of trajectory tracking. The energy-based Lyapunov function is selected to prove the stability and collision avoidance ability of the proposed control law. Moreover, we show that the maneuverability of the multi-agent system is enormously improved with the additional Hessian matrix term. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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High-fidelity parametric amplification of Ince–Gaussian beams

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Chinese Optics Letters 2022年第11期20卷 105-109页

作者： Ding Yan Zhiyuan Zhong Tong Qi Hongying Chen Wei Gao Heilongjiang Provincial Key Laboratory of Quantum Control School of Measurement and Communication EngineeringHarbin University of Science and TechnologyHarbin 150080China

Ince–Gaussian (IG) beams, as eigenfunctions of the paraxial wave equation in elliptical coordinates, are attracting increasing interest owing to their propagation-invariant and full-field properties. Optical amplification via parametric interactions can further expand their application areas, yet it is rarely studied. In this work, we report on a high-fidelity parametric amplifier for IG beams. The nonlinear transformation of the spatial spectra of the signal and associated influences on the beam profiles of the amplified signal, under different pump structures, were theoretically and experimentally investigated. By using a perfect flattop beam as the pump, we show that the transverse structure of IG signals is well maintained,and the distortion induced by radial-mode degeneration is overcome during amplification. This proof-of-principle demonstration paves the way for a mode-independent and distortion-free amplifier of arbitrary structured light and has great significance in relevant areas, such as quantum optics, tunable infrared-laser generation, and image amplification.

关键词： optical parametric amplification Ince–Gaussian beam high fidelity perfect flattop beam

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Multiclass Classification of Remote Sensing Images Using Deep Learning Techniques

Multiclass Classification of Remote Sensing Images Using Dee...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tahir Arshad Zhang Junping Qingyan Wang School of Electronics and Information Engineering Harbin Institute of Technology China School of Measurement-Control and Communication Engineering Harbin University of Science and Technology

Deep learning has strong learning ability to extract the features from Image datasets. In recent years, deep networks especially deep convolutional neural networks have revolutionized this field. When exposed to a huge number of datasets and their labels, deep learning techniques like Convolutional Neural Networks (CNNs) can produce precise categorization results. However, employing CNNs with scant labeled data might have a number of issues, including the problem of heavy overfitting. Convolutional Neural Networks are the backbone of modern deep learning architectures for the purpose of image classification. We solved image classification problem with different architectures and compare their performances. The objective of this work lies in the approach to study the given datasets. We use domain adaptation approach to highlight the underlying characteristics of these datasets and the different parameters (activation function, weights, regularization, neural simulation etc.) associated with these architectures. In our work, we use three datasets—RS19, UC Merced, and EuroSat images—were utilized in the CNN implementation to training the suggested model. The obtained results effectively demonstrate the local representation capacity of CNNs. Furthermore, this work shows that transfer learning improves classification outcomes in optical remote sensing images, particularly when the training sample is small.

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