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7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering

作者： Zhang, Jie Wu, Jing Province's Key Research Laboratory Intelligent Robot Hubei Wuhan430000 China Wuhan Institute of Technology CS Hubei Wuhan430000 China

ISBN: (纸本)9781510656437

In order to improve the health monitoring effect for patients with chronic diseases, and make it more comprehensive and efficient, this paper proposes the design and implementation of a smart medical system that can prevent and monitor chronic diseases. The system has friendly interface and simple operation. Through big data technology, it can monitor patients' physical signs data in real time and make correlation analysis of patients' medical history and historical physical signs data, so as to effectively and timely assess and intelligent early warning of patients' health. © 2022 SPIE. All rights reserved.

关键词： Big data

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Noise Adaption Network for Morse Code Image Classification

arXiv

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arXiv 2024年

作者： Wang, Xiaxia Leng, XueSong Xu, Guoping Hubei Key Laboratory of Intelligent Robot School of Computer Sciences and Engineering Wuhan Institute of Technology Hubei Wuhan China

The escalating significance of information security has underscored the pervasive role of encryption technology in safeguarding communication content. Morse code, a well-established and effective encryption method, has found widespread application in telegraph communication and various domains. However, the transmission of Morse code images faces challenges due to diverse noises and distortions, thereby hindering comprehensive classification outcomes. Existing methodologies predominantly concentrate on categorizing Morse code images affected by a single type of noise, neglecting the multitude of scenarios that noise pollution can generate. To overcome this limitation, we propose a novel two-stage approach, termed the Noise Adaptation Network (NANet), for Morse code image classification. Our method involves exclusive training on pristine images while adapting to noisy ones through the extraction of critical information unaffected by noise. In the initial stage, we introduce a U-shaped network structure designed to learn representative features and denoise images. Subsequently, the second stage employs a deep convolutional neural network for classification. By leveraging the denoising module from the first stage, our approach achieves enhanced accuracy and robustness in the subsequent classification phase. We conducted an evaluation of our approach on a diverse dataset, encompassing Gaussian, salt-and-pepper, and uniform noise variations. The results convincingly demonstrate the superiority of our methodology over existing approaches. The datasets are available on https://***/apple1986/MorseCodeImageClassify Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning of Gough-Stewart Platform

arXiv

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arXiv 2024年

作者： Zhu, Huizhi Xu, Wenxia Huang, Jian The Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

In this paper, we propose a graph neural network, DisGNet, for learning the graph distance matrix to address the forward kinematics problem of the Gough-Stewart platform. DisGNet employs the k-FWL algorithm for message-passing, providing high expressiveness with a small parameter count, making it suitable for practical deployment. Additionally, we introduce the GPU-friendly Newton-Raphson method, an efficient parallelized optimization method executed on the GPU to refine DisGNet’s output poses, achieving ultra-high-precision pose. This novel two-stage approach delivers ultra-high precision output while meeting real-time requirements. Our results indicate that on our dataset, DisGNet can achieves error accuracys below 1mm and 1deg at 79.8% and 98.2%, respectively. As executed on a GPU, our two-stage method can ensure the requirement for real-time computation. Codes are released at https://***/FLAMEZZ5201/DisGNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Newton-Raphson method

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Point-voxel dual transformer for LiDAR 3D object detection

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Optoelectronics Letters 2025年

作者： Tong Jigang Yang Fanhang Yang Sen Du Shengzhi Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot La-boratory， Tianjin University of Technology， 391 Binshui West Road Department of Electrical Engineering， Tshwane University of Technology

Deep learning for point clouds faces the challenge of their inherent unordered nature， which makes traditional CNN-like methods not directly applicable. However， due to the inherent permutation invariance， transformers provide solutions to unordered points problems faced in LiDAR-based object detection. In this paper， a two-stage LiDAR 3D object detection framework is presented， namely Point-Voxel Dual Transformer (PV-DT3D)， which is a transformer-based method. In the proposed PV-DT3D， point-voxel fusion features are used for proposal refinement. Specifically， in the PV-DT3D， keypoints are sampled from entire point cloud scene and used to encode representative scene features via a proposal-aware voxel set abstraction module. Subsequently， following the generation of proposals by the region proposal networks (RPN)， the internal encoded keypoints are fed into dual transformer encoder-decoder architecture. In 3D object detection， the proposed PV-DT3D is the first to take advantage of both pointwise transformer and channel-wise architecture to capture contextual information from the perspective of spatial and channel dimensions. Experiments conducted on the highly competitive KITTI 3D car detection leaderboard show that， the PV-DT3D achieves superior detection accuracy among state-of-the-art point-voxel-based methods.

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Study on Decentralization of Spherical Amphibious Multi-robot Control System Based on Smart Contract and Blockchain

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Journal of Bionic Engineering 2021年第6期18卷 1317-1330页

作者： Shuxiang Guo Sheng Cao Jian Guo Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of TechnologyBinshui Xidao Extension 391Tianjin300384China Intelligent Mechanical Systems Engineering Department Faculty of Engineering Kagawa UniversityTakamatsuKagawaJapan

With the development of intelligent bionic robots and the improvement of military application,a single robot cannot meet the requirements of the tasks of the current *** more complex tasks require not only that the robot be able to pass through the field barriers and the amphibious environment,but also that the robot be able to collaborate in a multi-robot ***,research on the multi-robot control system of spherical amphibious robots is very ***,the main research on amphibious robots is to improve the functions of a single robot,in the absence of the study of the multi-robot control *** systems primarily use a centralized control *** the transfer of central node can be achieved,there is still a problem of Byzantine fault tolerance in military applications,that is,when the amphibious multi-robot system is invaded by the *** central node may not only fail to accomplish the task,but also lose control of other robots,with severe *** solve the above problems,this paper proposed a decentralized method of spherical amphibious multi-robot control system based on blockchain ***,the point-to-point information network based on long range radio technology of low power wide area network was set up,we designed the blockchain system for embedded application environment and the decentralized hardware and software architecture of multi-robot control *** this basis,the consensus plugin,smart contract and decentralized multi-robot control algorithm were designed to achieve *** experimental results of consensus of spherical amphibious multi-robot showed the effectiveness of the decentralization.

关键词： Amphibious robot Blockchain Smart contract Control system Decentralization

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A Vital Signs intelligent Detection Framework of FMCW MIMO Radar Based on 3-D Beamforming and Muli-site Fusion of Heartbeat Signals

A Vital Signs Intelligent Detection Framework of FMCW MIMO R...

引用

Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Zijia Wang Pengcheng Gong Wuhan Institute of Technology(WIT) Wuhan Institute of Technology Wuhan China the Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

This paper presents a framework for frequency modulated continuous wave (FMCW) multiple input multiple output (MIMO) radar non-contact vital sign monitoring to achieve accurate heart rate (HR) in low SNR environments. The framework consists of two main parts. The first part includes a 3-D cardiac beamformer based on heartbeat signal quality, which is a beamformer composed of the distance dimension, azimuth and zenith angles. Proposed a new beamformer method and combined with the heart signal quality evaluation model to focus the radar array beam on the human heart to improve the signal quality. The second part proposed the multipart signal fusion (MSF) method, which fuses the reflection intensity of different parts of the trunk to improve the signal-to-noise ratio, thereby improving the performance of BR and HR. Finally, the HR and BR signals are separated by filter.

关键词： Three-dimensional displays Heart beat MIMO radar Estimation Radar Reflection MIMO Time-domain analysis Monitoring Signal to noise ratio

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Aerodynamic trajectory simulation system for composite sounding equipment 24

Aerodynamic trajectory simulation system for composite sound...

引用

Proceedings of the 2024 4th International Conference on Computational Modeling, Simulation and Data Analysis

作者： Chun Hu Jinyang Li Dezhi Zheng Peng Peng MIIT Key Laboratory of Complex-field Intelligent Exploration Beijing Institute of Technology Beijing China Chongqing Caeri Robot Technology Co Chongqing China

ISBN: (纸本)9798400711831

The initial exploration of aerodynamic models in sounding rocket flight dynamics is undertaken in this study. This includes a comprehensive modeling and analysis of the probe-carrying parachute system deployed during descent, as well as an investigation into the aerodynamic model of unmanned aerial vehicles carried by airships. Using OSGEarth and QT, a visualization simulation platform is established, integrating high-definition digital Earth imagery and constructing foundational backgrounds. This effort encompasses the creation of simulation animations and basic feature designs, along with the establishment of real-time communication interfaces between platforms. Additionally, a user-friendly human-machine interface is implemented, incorporating several practical functions to enhance usability and improve human-machine interaction experiences.

关键词： Aerodynamic model

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Registration of Lithium Battery x-ray Images Based on an Improved RANSAC Algorithm 21

Registration of Lithium Battery x-ray Images Based on an Imp...

引用

5th International Conference on Electronic Information technology and Computer Engineering, EITCE 2021

作者： DIng, Chang Chen, Deng Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9781450384322

Image registration and stitching are required in the defect detection of lithium batteries. However, existing image registration methods will suffer from a large number of mismatches caused by similar textures and structures in lithium battery x-ray images. In order to address the problem, we propose an improved RANSAC (random sample consensus, RANSAC) algorithm, which is optimized based on the structure characteristics of lithium battery. When solving the relationship model, this method calculates the matching quality of the matching pair based on the longitudinal pixel distance of the matching pair, and then eliminates the wrong matching point pairs according to the matching quality of the matching pair, thereby reducing the number of mismatched pairs. Experiments show that the algorithm proposed in this paper can eliminate obvious mismatched pairs, and the registration accuracy of lithium battery X-ray digital images is improved by 20.5% on average. © 2021 ACM.

关键词： Image registration

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Multi Scale SAR Image Target Detection Algorithm Based on RT-DETR

Multi Scale SAR Image Target Detection Algorithm Based on RT...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Wenhao Zhao Gengsheng Zheng School of Computer Science and Engineering School of Artificial Intelligence Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

To address low detection accuracy in SAR images due to complex backgrounds, large scale variations, and small targets, an improved algorithm SRA-DETR based on RT-DETR is proposed. It uses repBlock on four backbone branches to reduce parameters and enhance speed, incorporates a self-attention vision transformer for feature similarity extraction, and employs SE-CBAM for small target detection to suppress background interference. Adaptive path downsampling improves feature retention and background filtering compared to nearest neighbor methods. SRA-DETR decreases parameters by 40% and computational complexity by 20%, achieving aP50 of 96.6% and increasing mAP from 77.9% to 80.1% over the original RT-DETR.

关键词： Accuracy Soft sensors Object detection Interference Nearest neighbor methods Feature extraction Transformers Radar polarimetry Remote sensing Testing

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Pose Estimation of Six-axis Industrial robots Based on Deep Learning 21

Pose Estimation of Six-axis Industrial Robots Based on Deep ...

引用

5th International Conference on Electronic Information technology and Computer Engineering, EITCE 2021

作者： Yang, Yichen Wei, Wei Chen, Deng Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9781450384322

Industrial robot pose estimation has important applications in industrial robot safety detection and abnormal pose analysis. Aiming at the current problems of low accuracy of industrial robot pose estimation, this paper proposes a pose estimation method of six-axis industrial robots based on deep learning. Firstly, this paper uses the object detection method to detect the six joint axes of the industrial robot, and then the detected results are converted into the pose of the industrial robot. Additionally, this paper constructs an image dataset for industrial robot pose. Our method lays the foundation for industrial robot safety detection and abnormal pose analysis. The experimental results on the self-constructed dataset show that our method can accurately estimate the pose of the industrial robot, and the mean average precision (mAP) is 82.98%. It also outperforms previous industrial robot pose estimation methods by a significant margin of 29.98% performance gain. © 2021 ACM.

关键词： Industrial robots

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