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SPIE 12th International Symposium on multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Zou, Lamei Wang, Hao Xie, Jia Wu, Chengqing Lu, Luhan Guo, Yichun National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

We present a two-dimensional human pose estimation network constrained by the human structure information (HSINet). HSINet effectively fuses features of different scales and explicitly integrates human structure information to enhance the precision of key point localization. The architecture of HSINet comprises three pivotal modules: The feature extraction module, the encoding module, and the decoding module. The feature extraction module within HSINet employs the architecture of High-Resolution Net (HRNet). In contrast to HRNet, we remove redundant layers, and enhance the ability to combine global features and local features using the Gated Attention Unit (GAU). The second module encodes the feature maps derived from the feature extraction module. Each feature map corresponds to a joint point and is characterized by two feature vectors representing the x and y axes. Utilizing graph convolution for encoding introduces constraints based on human structure information. Subsequently, these encoded feature maps are decoded into precise coordinates of key points. The experiment results on COCO datasets show that our proposed method can improve the precision of key point detection while effectively reducing the number of parameters. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Feature extraction

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A shape and texture feature blending network for bone marrow cells classification using auxiliary learning 12

A shape and texture feature blending network for bone marrow...

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SPIE 12th International Symposium on multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Guo, Zhengyang Yang, Zengbiao Xiang, Xuanyu Tan, Yihua National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510674981

ISBN: (纸本)9781510674974

Morphological examination of bone marrow cells is crucial for diagnosing blood diseases. However, manual classification of bone marrow cells is time-consuming and subjective. Therefore, it is necessary to develop an autoclassification method for bone marrow cells. Although deep learning methods are commonly used for cell classification like Resnet50, they don't take advantage of the features of bone marrow cells such as the shape features of cells. However, the shape of cell and nucleus plays a significant role in distinguishing between different cell types. In this paper, we proposed a Shape and Texture Feature Blending Network (STFB-Net) for bone marrow cells classification based on auxiliary learning. We used ResNext50 as the backbone network for STFB-Net due to its exceptional ability to extract texture features from cells. In addition, we proposed the Shape Feature Extraction Module (SFEM) to enhance the backbone network's ability for capturing shape features. SFEM shares a part of parameters with the backbone network. SFEM extracts features at multiple scales and up-samples them, then fuses multi-scale features to predict the shape of cells. We performed experiments on two bone marrow cell datasets. The results show that the proposed STFB-Net can effectively extract texture and shape features, which brings better performance than other cell classification methods. By using the Grad-CAM method to visualize the features extracted by STFB-Net, we proved the reliability and the effectiveness of the STFB-Net in extracting cell shape features. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Feature extraction

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Secure data rate maximization for full-duplex UAV-enabled base station

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Digital Communications and Networks 2024年第5期10卷 1387-1393页

作者： Chunlong He Xinjie Li Yin Huang Jianzhen Lin Gongbin Qian Xingquan Li Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhenChina National Mobile Communications Research Laboratory Southeast UniversityNanjing211189China Shenzhen Institute of Information Technology ShenzhenChina

Unmanned Aerial Vehicle(UAV)is an air base station featuring flexible deployment and *** can significantly improve the communication quality of the system due to its line-of-sight channel connection with ground ***,due to the openness of UAV-to-Ground channels,the communication between ground users'devices and UAV is easily *** this paper,we aim to improve the security of communication system by using full-duplex UAV as a mobile air base *** UAV sends interference signals to eavesdroppers and receives signals from ground *** jointly optimize the scheduling between the UAV and ground devices,the transmission power of the UAV and ground devices,as well as the trajectory of the UAV to maximize the minimum average security communication data *** optimization problem is mixed with integers and nonconvex ***,this problem is not a standard convex optimization problem,which can not be solved with standard *** this in mind,we propose an effective algorithm which solves this problem iteratively by applying Successive Convex Approximation(SCA),variable relaxation and ***,numerical results demonstrate the effectiveness of the proposed algorithm.

关键词： UAV relay Physical layer security Full-duplex UAV Power optimization Trajectory optimization

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Time-varying Formation-containment Tracking Control of Heterogeneous Multi-agent System under Denial of Service Attack and Sensor Fault

Time-varying Formation-containment Tracking Control of Heter...

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2024 China Automation Congress, CAC 2024

作者： Niu, Baojian Fan, Huijin Liu, Lei Wang, Bo National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350368604

In this paper, the problem of output-feedback time-varying formation-containment tracking control for heterogeneous multi-agent systems with sensor fault under distributed denial of service (DoS) attacks is studied. The leaders are non-cooperative, which are embedded with unknown input. Firstly, in order to estimate the sensor fault and the states of the followers, a distributed fault-detecting observer is designed. Secondly, in order to deal with distributed DoS attacks and the unknown input of the leaders, a leader state estimator is designed. Finally, a distributed output-feedback containment control law is proposed to realize the time-varying formation tracking of the convex hull of multiple leaders. It is proved that the proposed control scheme guarantees the exponential convergence of all errors of the multi-agent system, and the effectiveness and superiority of the proposed scheme are verified by simulation experiments. © 2024 IEEE.

关键词： Denial-of-service attack

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Relative Position-based Distributed Fixed-time Formation Control of Multiple UAVs under GNSS-denied environment 43

Relative Position-based Distributed Fixed-time Formation Con...

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43rd Chinese Control Conference, CCC 2024

作者： Yang, Jiahao Zhi, Yongran Xue, Chenlong Liu, Lei Fan, Huijin Wang, Bo Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

In this paper, a novel fixed-time formation control algorithm is proposed for multiple unmanned aerial vehicles (UAVs) in environments where global navigation satellite system(GNSS) is denied, where the absolute positions of UAVs are unmeasurable, and only relative positions are accessible. To address this challenge, firstly, a composite observers is designed with one estimating relative positions between leader and followers and the other being a distributed fixed-time velocity observer. The integration of these observers enables each follower to obtain relative position and velocity of the leader within a fixed time. Subsequently, based on the composite observers and the desired formation matrix, a fixed-time controller is formulated aiming to achieve the formation task within a fixed time. Furthermore, Lyapunov stability theory demonstrates that the designed observers and controller can successfully accomplish the formation task within a fixed time. Finally, numerical simulations validate the effectiveness of the analytical results. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Unmanned aerial vehicles (UAV)

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Adaptive Tracking of Uncertain Markovian Jump Nonlinear Systems With Random Actuator Faults

Adaptive Tracking of Uncertain Markovian Jump Nonlinear Syst...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Jiao-Yang Fan, Huijin Liu, Lei Wang, Bo National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798350368604

This article deals with the adaptive fault-tolerant tracking problem for uncertain Markovian jump nonlinear systems (MJNSs) in a stochastic framework. A joint and a scalar Markovian process are employed to describe behaviors of the unknown parameters and actuator faults, respectively, thereby characterizing uncertainties in a random manner. With the aid of the backstepping technique, an adaptive tracking controller as well as the corresponding parameter update laws is constructed by treating the regimes of all Markovian variables as constants. It is shown that with appropriate design parameters, the output tracking error can converge into a small adjustable residual set in the sense of mean quadratic value while all closed-loop signals are bounded in probability, regardless of infinite number of random abrupt jumps in both uncertain parameters and the control input. An illustrative example is provided to show the effectiveness of the theoretical results. © 2024 IEEE.

关键词： Markov processes

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An Edge-Guided Network for Building Footprint Extraction from Optical Remote Sensing Images 12

An Edge-Guided Network for Building Footprint Extraction fro...

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SPIE 12th International Symposium on multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Zhou, Yuhang Tan, Yihua National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Accurate building footprint extraction from optical remote sensing images remains challenging due to the diverse appearance and complex scenarios. Although recent deep learning-based methods have been shown to greatly improve the accuracy of building footprint extraction, vanilla deep networks still suffer from ambiguous predictions of edge pixels. The building edge contains abundant location and shape information, which is important for downstream applications such as building positioning and area measurement. Therefore, the problem of inaccurate edge prediction needs to be resolved urgently. To this end, we propose a novel edge-guided network (EGNet) that makes ample use of the edge prior in an end-to-end manner. First, an edge extraction module (EEM) is proposed to extract the building edge map. Then, an edge-guidance module (EGM) is designed to utilize the edge map to guide each encoder block in extracting edge-related features. Furthermore, a multi-scale context aggregation module (MCAM) is built to enhance the feature representation by aggregating contextual semantics with different receptive fields. EGNet can effectively mine edge semantics and guide the representation learning of boundaries, achieving 75.21% and 91.16% IoU on the Massachusetts and WHU datasets, respectively. Experimental results demonstrate that the proposed EGNet has a certain superiority in both accuracy and efficiency compared with current state-of-the-art (SOTA) methods. © 2024 SPIE.

关键词： Extraction

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Observer-based Hierarchical Control of Heterogeneous Multiagent Systems under Attacks and Disturbances 43

Observer-based Hierarchical Control of Heterogeneous Multiag...

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43rd Chinese Control Conference, CCC 2024

作者： Zhi, Yongran Fan, Huijin Liu, Lei Wang, Bo Huazhong University of Science and Technology The National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

The resilient cooperative output regulation of heterogeneous leader-follower multiagent systems (MASs) under asynchronous DoS attacks and unknown external disturbances is investigated in this paper. An observer-based hierarchical control frame is constructed, which contains communication layer and controller layer. In the communication layer, based on an improved dynamic event-triggered mechanism, a novel fully distributed observer is proposed to estimate the leader's state under the asynchronous DoS attack. In the controller layer, an anti-disturbance state observer is firstly designed to estimate the follower's state in the existence of unknown external disturbances, then an output feedback controller is constructed to track the estimate leader's state. The proposed two observers has increased the tolerance to DoS attacks and external disturbances. Finally, simulation results have demonstrated the effectiveness of the proposed hierarchical control frame. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Feedback control

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Unpaired Thick Cloud Removal with Multiscale SAR-optical Data Fusion 5

Unpaired Thick Cloud Removal with Multiscale SAR-optical Dat...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Xiang, Xuanyu Tan, Yihua Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9798350373820

Cloud removal is vital for the analysis of optical satellite images. To alleviate the impact of thick clouds, recent advances integrate deep learning withmultimodal data. To relax the requirements of paired training samples, existing methods utilized cycle-consistent architecture to learn the relation between cloudy and cloud-free images based on unpaired training samples. When considering thick cloud removal based on unpaired training data, the relevant studies are insufficient and they face two challenges: 1) The information in non-cloudy areas is not preserved well after cloud removal;2) The recovered information lacks consistency with the global textures and structures. Based on optical-SAR fusion and cycle-consistent training, this paper proposes Multiscale Cycle-consistent Fusion (MCF) model. MCF designs preservation loss to overcome the first challenge, and proposes global-local discriminator combined with Parallel Dilated Channel-weighted Module (PDCM) for the second challenge. MCF is evaluated both on a simulated dataset and a real dataset, which demonstrates its effectiveness. © 2024 IEEE.

关键词： Image fusion

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EPM-Net: A better way for optical and infrared scene matching via edge point 12

EPM-Net: A better way for optical and infrared scene matchin...

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SPIE 12th International Symposium on multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Huang, Shixiang Chi, Longyun Fu, Wenxing Tian, Jinwen National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

As a crucial auxiliary technique in inertial navigation, scene matching has been widely applied in aircraft navigation and guidance. To enhance the adaptability and efficiency of scene matching algorithms in complex environments, existing approaches have gradually shifted towards heterogeneous scene matching, such as optical/ infrared scene matching based on deep learning features, with a focus on point features. However, due to the complex and diverse nonlinear distortions between optical and infrared images, the matching accuracy often fails to meet the practical requirements of navigation and positioning. In view of this problem, an EPM-Net algorithm based on edge features is proposed to improve matching performance by extracting more stable and discriminative edge point descriptors. Experimental results on infrared datasets demonstrate that this method achieves an average matching accuracy improvement of more than four times compared to traditional and existing deep learning methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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