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arXiv 2024年

作者： Luo, Jing Mao, Qi Shi, Weiwei Shi, Zhenghao Wang, Xiaofan Lu, Xiaofeng Hei, Xinhong Shaanxi Key Laboratory for Network Computing and Security Technology and Human–Machine Integration Intelligent Robot Shaanxi University Engineering Research Center School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an China

While deep learning models have been extensively utilized in motor imagery based EEG signal recognition, they often operate as black boxes. Motivated by neurological findings indicating that the mental imagery of left or right-hand movement induces event-related desynchronization (ERD) in the contralateral sensorimotor area of the brain, we propose a Mirror Contrastive Loss based Sliding Window Transformer (MCL-SWT) to enhance subject-independent motor imagery-based EEG signal recognition. Specifically, our proposed mirror contrastive loss enhances sensitivity to the spatial location of ERD by contrasting the original EEG signals with their mirror counterparts—mirror EEG signals generated by interchanging the channels of the left and right hemispheres of the EEG signals. Moreover, we introduce a temporal sliding window transformer that computes self-attention scores from high temporal resolution features, thereby improving model performance with manageable computational complexity. We evaluate the performance of MCL-SWT on subject-independent motor imagery EEG signal recognition tasks, and our experimental results demonstrate that MCL-SWT achieved accuracies of 66.48% and 75.62%, surpassing the state-of-the-art (SOTA) model by 2.82% and 2.17%, respectively. Furthermore, ablation experiments confirm the effectiveness of the proposed mirror contrastive loss. A code demo of MCL-SWT is available at https://***/roniusLuo/MCL_SWT. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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A distributed drive articulated robot control system based on steering control strategy

A distributed drive articulated robot control system based o...

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IEEE International Conference on Mechatronics and Automation

作者： Yunfei Su Hongchang Sun Yongxiang Jiang Longfei Wei Sanpeng Deng Hao Sun Dongcheng Ren Institute of Robot and Intelligent Equipment Tianjin Intelligent Robot Technology and Application Enterprise Key Laboratory School of Mechanical Engineering Tianjin University of Technology and Education China Engineering Research Center of the Ministry of Education of China in Intelligent Rehabilitation Equipment and Detection Technologies School of Artificial Intelligence Hebei University of Technology China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

In order to improve the motion control characteristics of articulated rock-drilling robot, an articulated robot chassis structure with encoder expansion link and distributed electric wheel is designed, and a steering control strategy based on push rod pose feedback is proposed. Based on Adams/Simulink co-simulation, the steering control strategy model is built. The motion of the articulated rock drilling robot in various working conditions is analyzed by co-simulation experiment. Compared with the traditional control strategy, the steering time of the articulated rock-drilling robot by the steering control strategy is increased to 13.87%, and the steering distance is reduced to 16.95%, which effectively improves the steering ability of the robot.

关键词： Drilling robot control Wheels Rocks Mathematical models Mobile robots Motion control

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P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose Uncertainty

arXiv

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arXiv 2024年

作者： Zhang, Yufan Yang, Kailun Wang, Ze Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

This paper presents P2U-SLAM, a visual Simultaneous Localization And Mapping (SLAM) system with a wide Field of View (FoV) camera, which utilizes pose uncertainty and point uncertainty. While the wide FoV enables considerable repetitive observations of historical map points for matching cross-view features, the data properties of the historical map points and the poses of historical keyframes have changed during the optimization process. The neglect of data property changes triggers the absence of a partial information matrix in optimization and leads to the risk of long-term positioning performance degradation. The purpose of our research is to reduce the risk of the wide field of view visual input to the SLAM system. Based on the conditional probability model, this work reveals the definite impact of the above data properties changes on the optimization process, concretizes it as point uncertainty and pose uncertainty, and gives a specific mathematical form. P2U-SLAM respectively embeds point uncertainty and pose uncertainty into the tracking module and local mapping, and updates these uncertainties after each optimization operation including local mapping, map merging, and loop closing. We present an exhaustive evaluation in 27 sequences from two popular public datasets with wide-FoV visual input. P2U-SLAM shows excellent performance compared with other state-of-the-art methods. The source code will be made publicly available at https://***/BambValley/P2U-SLAM. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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MA-VoxelMorph:Multi-scale attention-based VoxelMorph for nonrigid registration of thoracoabdominal CT images

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Journal of Innovative Optical Health Sciences 2025年第1期18卷 135-151页

作者： Qing Huang Lei Ren Tingwei Quan Minglei Yang Hongmei Yuan Kai Cao School of Computer Science&Engineering Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan Hubei 430205P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Wuhan Hubei 430074P.R.China Beijing Wandong Medical Technology Co. Ltd.Beijing 100015P.R.China Changhai Hospital of Shanghai Shanghai 200433 P.R.China

This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization ***,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is *** deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration *** alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels *** further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding *** method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local *** proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine *** also proved to be fast in 3D image registration,using about 1.5 s,and faster than most *** and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.

关键词： Thoracoabdominal CT image registration large deformation fine structure multi-scale attention mechanism

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DISD-Net: A Dynamic Interactive Network with Self-distillation for Cross-subject Multi-modal Emotion Recognition

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IEEE Transactions on Multimedia 2025年

作者： Cheng, Cheng Liu, Wenzhe Wang, Xinying Feng, Lin Jia, Ziyu Liaoning Normal University Research Center of Brain and Cognitive Neuroscience Dalian116029 China Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei230601 China Key Laboratory of Biomedical Engineering of Hainan Province Haikou570228 China Huzhou University School of Information Engineering Huzhou313000 China Dalian University of Technology School of Computer Science and Technology Dalian116081 China Institute of Automation Chinese Academy of Sciences Brainnetome Center Beijing100190 China

Multi-modal Emotion Recognition (MER) has demonstrated competitive performance in affective computing, owing to synthesizing information from diverse modalities. However, many existing approaches still face unresolved challenges, such as: (i) how to learn compact yet representative features from multi-modal data simultaneously, and (ii) how to address differences among subjects and enhance the generalization of the emotion recognition model, given the diverse nature of individual biological signals. To this end, we propose a Dynamic Interactive Network with Self-Distillation (DISD-Net) for cross-subject MER. The DISD-Net incorporates a dynamin interactive module to capture the intra- and inter-modal interactions from multi-modal data. Additionally, to enhance compactness in modal representations, we leverage the soft labels generated by the DISD-Net model as supplemental training guidance. This involves incorporating self-distillation, aiming to transfer the knowledge that the DISD-Net model contains hard and soft labels to each modality. Finally, domain adaptation (DA) is seamlessly integrated into the dynamic interactive and self-distillation components, forming a unified framework to extract subject-invariant multi-modal emotional features. Experimental results indicate that the proposed model achieves a mean accuracy of 75.00% with a standard deviation of 7.68% for the DEAP dataset and a mean accuracy of 65.65% with a standard deviation of 5.08% for the SEED-IV dataset. © 1999-2012 IEEE.

关键词： domain adaptation (DA) dynamic interactive Multi-modal Emotion Recognition (MER) self-distillation

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A 3D flexible neural interface based on a microfluidic interconnection cable capable of chemical delivery

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Microsystems & Nanoengineering 2021年第4期7卷 169-179页

作者： Yoo Na Kang Namsun Chou Jae-Won Jang Han Kyoung Choe Sohee Kim Department of Medical Assistant Robot Korea Institute of Machinery&Materials(KIMM)DaeguRepublic of Korea Center for BioMicrosystems Korea Institute of Science and Technology(KIST)SeoulRepublic of Korea Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology(DGIST)DaeguRepublic of Korea Department of Brain&Cognitive Sciences Daegu Gyeongbuk Institute of Science and Technology(DGIST)DaeguRepublic of Korea

The demand for multifunctional neural interfaces has grown due to the need to provide a better understanding of biological mechanisms related to neurological diseases and neural *** intracerebral drug injection using microfluidic neural interfaces is an effective way to deliver drugs to the brain,and it expands the utility of drugs by bypassing the blood-brain barrier(BBB).In addition,uses of implantable neural interfacing devices have been challenging due to inevitable acute and chronic tissue responses around the electrodes,pointing to a critical issue still to be *** neural interfaces comprised of a collection of microneedles in an array have been used for various applications,it has been challenging to integrate microfluidic channels with them due to their characteristic three-dimensional structures,which differ from two-dimensionally fabricated shank-type neural *** we present a method to provide such three-dimensional needle-type arrays with chemical delivery *** fabricated a microfluidic interconnection cable(pFIC)and integrated it with a flexible penetrating microelectrode array(FPMA)that has a 3-dimensional structure comprised of silicon microneedle electrodes supported by a flexible array *** successfully demonstrated chemical delivery through the developed device by recording neural signals acutely from in vivo brains before and after KCl *** suggests the potential of the developed microfluidic neural interface to contribute to neuroscience research by providing simultaneous signal recording and chemical delivery capabilities.

关键词： neural recording needle

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Beyond the Field-of-View: Enhancing Scene Visibility and Perception with Clip-Recurrent Transformer

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-16页

作者： Shi, Hao Jiang, Qi Yang, Kailun Yin, Xiaoting Ni, Huajian Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation and the National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou China School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China Shanghai SUPREMIND Technology Company Ltd Shanghai China

Vision sensors are widely applied in vehicles, robots, and roadside infrastructure. However, due to limitations in hardware cost and system size, camera Field-of-View (FoV) is often restricted and may not provide sufficient coverage. Nevertheless, from a spatiotemporal perspective, it is possible to obtain information beyond the camera's physical FoV from past video streams. In this paper, we propose the concept of online video inpainting for autonomous vehicles to expand the field of view, thereby enhancing scene visibility, perception, and system safety. To achieve this, we introduce the FlowLens architecture, which explicitly employs optical flow and implicitly incorporates a novel clip-recurrent transformer for feature propagation. FlowLens offers two key features: 1) FlowLens includes a newly designed Clip-Recurrent Hub with 3D-Decoupled Cross Attention (DDCA) to progressively process global information accumulated over time. 2) It integrates a multi-branch Mix Fusion Feed Forward Network (MixF3N) to enhance the precise spatial flow of local features. To facilitate training and evaluation, we derive the KITTI360 dataset with various FoV mask, which covers both outer- and inner FoV expansion scenarios. We also conduct quantitative assessments of beyond-FoV semantics across different models and perform qualitative comparisons of beyond-FoV object detection. We illustrate that employing FlowLens to reconstruct unseen scenes even enhances perception within the field of view by providing reliable semantic context. Extensive experiments and user studies involving offline and online video inpainting, as well as beyond-FoV perception tasks, demonstrate that FlowLens achieves state-of-the-art performance. The source code and dataset are made publicly available at https://***/MasterHow/FlowLens. IEEE

关键词： Cameras

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

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Military Medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

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Design of Solar Endurance Control for Unmanned Aerial Vehicles based on Schmidt Trigger Control

Design of Solar Endurance Control for Unmanned Aerial Vehicl...

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2023 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2023

作者： Hu, Xusheng Liu, Juan Kocheril, Rajesh Ma'Anshan Engineering Technology Research Center for Wireless Sensor Network and IntelliSense Ma'anshan243031 China Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot Wuhu241002 China Ma'Anshan Technical College Ma'anshan243030 China Toc H Institute of Science & Technology APJ Abdul Kalam Technological University Kerala India

In order to improve the endurance of unmanned aerial vehicles (UAVs), a power supply controller with automatic switching between solar energy and battery was designed. The controller is mainly composed of an AT89C51 single chip microcomputer. By collecting ambient light intensity and comparing it with the preset light intensity of the system, the controller completes automatic switching of the power supply for solar unmanned aerial vehicles, thereby achieving continuous and stable power supply for solar unmanned aerial vehicles. In addition, the implementation method of the single-chip microcomputer program in the controller is also introduced. The control core is to use Schmidt de shake control to switch between solar energy and battery, and to test the various functions of the controller through the flight experiment of a small solar powered unmanned aerial vehicle. Tests have found that solar powered drones using this controller can navigate for a long time in weather with varying light intensity, and the device operates well, effectively improving the power stability of solar powered drones. © Published under licence by IOP Publishing Ltd.

关键词： Controllers

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Research on Mobile robot Localization Algorithm with Improved ORB Extraction Matching

Research on Mobile Robot Localization Algorithm with Improve...

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IEEE International Conference on Mechatronics and Automation

作者： Hongchang Sun Longfei Wei Yongxiang Jiang Yunfei Su Sanpeng Deng Hao Sun Haihang Wang School of Mechanical Engineering Institute of Robot and Intelligent Equipment Tianjin Intelligent Robot Technology and Application Enterprise Key Laboratory TianJin University of Technology and Education Tianjin China School of Artificial Intelligence Engineering Research Center of the Ministry of Education of China in Intelligent Rehabilitation Equipment and Detection Technologies Hebei University of Technology Tianjin China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Aiming at the problem of inaccurate positioning accuracy of V-SLAM system caused by poor texture and blurred images generated by mobile robots during rapid movement and large angle rotation, an improved ORB feature extraction matching algorithm is proposed. Firstly, using the statistical form of information entropy to calculate the image features, image enhancement is used to highlight the feature information of poorly textured and blurred image blocks, improve the efficiency of feature point detection, and enhance the robustness of the system. In addition, to improve the accuracy of feature matching, the BEBLID descriptor is used instead of the BRIEF descriptor for feature description. Through experimental verification on the publicly available TUM data set, it was found that the improved algorithm has a higher matching accuracy than the ORB algorithm, and has a significant increase in algorithm time compared to the ORB algorithm. After trajectory calculation, the improved algorithm is more in line with the real trajectory in camera motion and has a good improvement in pose accuracy.

关键词： Visualization Accuracy robot vision systems Feature extraction Odometers Robustness Trajectory

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