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Mechanically and Conductively Robust Eutectogel Fiber Produced by Continuous Wet Spinning Enables Epidermal and Implantable Electrophysiological Monitoring

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Advanced Fiber Materials 2024年第6期6卷 1980-1991页

作者： Shufeng Hu Jingya Song Qiong Tian Chen Zeng Yuchen Jiang Qihua Li Jun Xu Wei Yan Jun Li Zhiyuan Liu Weiqing Kong Meifang Zhu State Key Laboratory of Pulp and Paper Engineering Plant Fiber Materials Science Research CenterSouth China University of TechnologyGuangzhou 510006China State Key Laboratory for Modification of Chemical Fibers&Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghai 201620China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligent-Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Standard Robots Co. LtdRoom 405Building DHuafeng International Robot Fusen Industrial ParkHangcheng AvenueGuxing CommunityXixiang StreetBaoan DistrictShenzhen 518000China Research Center for Neural Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

In recent years,the collection and monitoring of human physiological signals have garnered increasing attention due to their wide-ranging applications in healthcare,human-machine interaction,sports,and other ***,the continuous fabrication of flexible gel fiber electrodes with high mechanical performance,high conductivity,and durability for extreme environments using a simple,efficient,and universal strategy remains challenging for physiological signal ***,we have employed a strategy of solvent replacement and multi-level hydrogen bond enhancement to construct eutectogel fibers with continuous solid-liquid structure,achieving continuous production of fibers with high strength,high conductivity,and low-temperature *** the fiber,PVA serves as the solid-state elastic phase,DES as the liquid ionic conductive phase,and CNF as the reinforcement *** resulting eutectogel fibers exhibit excellent tensile strength(37.3 MPa),good elongation(>700%),high electrical conductivity(0.543 S/m),and resistance to extreme dry and-60°C low-tem-perature ***,these eutectogel fibers demonstrate high sensitivity for monitoring joint movements and effectively detecting in vitro and in vivo signals,show casing their potential for wearable strain sensors and monitoring physiological signals.

关键词： Eutectogel fibers Wet spinning Hydrogen-bond networks Sensing

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Research on Biomimetic Design Methods for Humanoid robot Thigh*

Research on Biomimetic Design Methods for Humanoid Robot Thi...

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IEEE International Conference on robotics and Biomimetics

作者： Daming Nie Anhuan Xie Lingyu Kong Yu Zhang Gang Zheng Yili Fu Jason Gu Intelligent Robot Research Center Zhejiang Laboratory Hangzhou China School of Mechanical and Energy Engineering Zhejiang University of Science and Technology Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

Human bones have formed the preferred configuration for high-strength and lightweight after long-time evolution. Taking human’s longest and strongest bone - the femur - as an example, it is consist of two characteristic layers, i.e. the substantia compacta and the substantia spongiosd. This article innovatively imitates the structural characteristics of human femur, the thigh of humanoid robot is designed in form of "variable thickness shell + variable density lattice". The thickness of shell and the density of lattice are adjusted by the initial stress distribution individually. Results show that the weight of shell and lattice of the thigh structure can be reduced by 20% under reasonable mapping relationship of "stress - shell thickness" and "stress - lattice rod diameter", while the structural stiffness meets the application requirements. Finally, the limiting factors of the "variable thickness shell + variable density lattice" structure designing approach are analyzed, and potential measures for optimizing the design method of the humanoid robot thigh in the future are described.

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GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understanding

arXiv

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arXiv 2023年

作者： Gu, Jia-Chen Ling, Zhen-Hua Liu, Quan Liu, Cong Hu, Guoping National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence China iFLYTEK Research Hefei China

Addressing the issues of who saying what to whom in multi-party conversations (MPCs) has recently attracted a lot of research attention. However, existing methods on MPC understanding typically embed interlocutors and utterances into sequential information flows, or utilize only the superficial of inherent graph structures in MPCs. To this end, we present a plug-and-play and lightweight method named graph-induced fine-tuning (GIFT) which can adapt various Transformer-based pre-trained language models (PLMs) for universal MPC understanding. In detail, the full and equivalent connections among utterances in regular Transformer ignore the sparse but distinctive dependency of an utterance on another in MPCs. To distinguish different relationships between utterances, four types of edges are designed to integrate graph-induced signals into attention mechanisms to refine PLMs originally designed for processing sequential texts. We evaluate GIFT by implementing it into three PLMs, and test the performance on three downstream tasks including addressee recognition, speaker identification and response selection. Experimental results show that GIFT can significantly improve the performance of three PLMs on three downstream tasks and two benchmarks with only 4 additional parameters per encoding layer, achieving new state-of-the-art performance on MPC understanding. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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SHINE: Syntax-augmented Hierarchical Interactive Encoder for Zero-shot Cross-lingual Information Extraction

arXiv

引用

arXiv 2023年

作者： Ma, Jun-Yu Gu, Jia-Chen Ling, Zhen-Hua Liu, Quan Liu, Cong Hu, Guoping National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence China iFLYTEK Research Hefei China

Zero-shot cross-lingual information extraction (IE) aims at constructing an IE model for some low-resource target languages, given annotations exclusively in some rich-resource languages. Recent studies based on language-universal features have shown their effectiveness and are attracting increasing attention. However, prior work has neither explored the potential of establishing interactions between language-universal features and contextual representations nor incorporated features that can effectively model constituent span attributes and relationships between multiple spans. In this study, a syntax-augmented hierarchical interactive encoder (SHINE) is proposed to transfer cross-lingual IE knowledge. The proposed encoder is capable of interactively capturing complementary information between features and contextual information, to derive language-agnostic representations for various IE tasks. Concretely, a multilevel interaction network is designed to hierarchically interact the complementary information to strengthen domain adaptability. Besides, in addition to the well-studied syntax features of part-of-speech and dependency relation, a new syntax feature of constituency structure is introduced to model the constituent span information which is crucial for IE. Experiments across seven languages on three IE tasks and four benchmarks verify the effectiveness and generalization ability of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Information retrieval

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Transform-domain Image Enhancement for Underwater Visual SLAM based on Homomorphic Filter

Transform-domain Image Enhancement for Underwater Visual SLA...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Shuo Zhang Chaoyu Zhang Wei Wang Xinrui Zeng Bin Luo State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China China Nuclear Power Operation Technology Corporation LTD Wuhan China CNNC Engineering Research Center of Nuclear Industry Special Robot Wuhan China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Underwater visual SLAM algorithms play a vital role in autonomously localizing Autonomous Underwater Vehicles (AUV) in challenging underwater environments. The low-light conditions underwater necessitate the use of artificial lighting sources. However, this can lead to issues such as overexposed or underexposed images, resulting in reduced image contrast and information content. This study introduces a transform-domain image enhancement technique aimed at bolstering the resilience of underwater visual simultaneous localization and mapping (SLAM) systems. Specifically, a homomorphic filtering method is implemented to alleviate the impact of low-frequency lighting variations on images, thereby enhancing overall image clarity and bolstering the performance of visual SLAM systems. The efficacy of this filtering approach is validated through experiments conducted on the AQUALOC dataset. Our findings demonstrate that this method enhances the success rate of visual SLAM tracking, thereby enhancing its utility in AUV localization tasks.

关键词： Location awareness Image quality Visualization Autonomous underwater vehicles Simultaneous localization and mapping Filtering Filtering algorithms Feature extraction Artificial light Image enhancement

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Rotation-Adaptive Point Cloud Domain Generalization via Intricate Orientation Learning

arXiv

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arXiv 2025年

作者： Liu, Bangzhen Zheng, Chenxi Xu, Xuemiao Xu, Cheng Zhang, Huaidong He, Shengfeng South China University of Technology Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology The State Key Laboratory of Subtropical Building and Urban Science The Ministry of Education Key Laboratory of Big Data and Intelligent Robot The Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China Singapore Management University Singapore

The vulnerability of 3D point cloud analysis to unpredictable rotations poses an open yet challenging problem: orientation-aware 3D domain generalization. Cross-domain robustness and adaptability of 3D representations are crucial but not easily achieved through rotation augmentation. Motivated by the inherent advantages of intricate orientations in enhancing generalizability, we propose an innovative rotation-adaptive domain generalization framework for 3D point cloud analysis. Our approach aims to alleviate orientational shifts by leveraging intricate samples in an iterative learning process. Specifically, we identify the most challenging rotation for each point cloud and construct an intricate orientation set by optimizing intricate orientations. Subsequently, we employ an orientation-aware contrastive learning framework that incorporates an orientation consistency loss and a margin separation loss, enabling effective learning of categorically discriminative and generalizable features with rotation consistency. Extensive experiments and ablations conducted on 3D cross-domain benchmarks firmly establish the state-of-the-art performance of our proposed approach in the context of orientation-aware 3D domain generalization. © 2025, CC BY-NC-SA.

关键词： Contrastive Learning

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Tightly-coupled Lidar-Visual-Inertial SLAM for Mobile robot

Tightly-coupled Lidar-Visual-Inertial SLAM for Mobile Robot

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Qiliang Du Bojie Chen Lianfang Tian Ling Yuan School of Automation Science and Engineering South China University of Technology Key Laboratory of Large-Model Embodied-Intelligent Humanoid Robot (2024KSYS004) Guangzhou China School of Automation Science and Engineering South China University of Technology Guangzhou China Guangdong Engineering Research Center of Cloud-Edge-End Collaboration Technology for Smart City Guangzhou China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Simultaneous Localization and Mapping (SLAM) enables robots to perform localization and mapping in unknown environments. Currently, mainstream single-sensor Lidar SLAM (Visual SLAM) tends to diverge and fail when faced with unstructured or textureless scenes. To address this issue, this paper proposes a robust tightly-coupled Lidar-Vision-Inertial Odometry framework (LRI-LVIO) that achieves high-precision and robust SLAM. LRI-LVIO consists of two subsystems: a visual-inertial system and a lidar-inertial system. These two subsystems are tightly coupled through an error-state iterative Kalman filter, allowing one subsystem to maintain stable operation even if the other fails, thus enhancing the robustness of SLAM in textureless and featureless environments. Additionally, the depth of visual feature points is recovered using lidar point cloud information, improving the algorithm's efficiency and accuracy. Finally, LRI-LVIO incorporates loop closure detection based on lidar point cloud keyframes, further reducing cumulative errors and enhancing localization precision. Experiments conducted in various indoor and outdoor environments show that by combining the advantages of each sensor, LRI-LVIO achieves higher accuracy and stronger robustness compared to single-sensor SLAM.

关键词： Point cloud compression Location awareness Visualization Simultaneous localization and mapping Laser radar Accuracy Machine learning algorithms Robustness Odometry Mobile robots

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Physics Engine-Based Whole-Hand Haptic Rendering for Sensorimotor Neurorehabilitation

Physics Engine-Based Whole-Hand Haptic Rendering for Sensori...

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World Haptics Conference

作者： Raphael Rätz Laura Marchal-Crespo Motor Learning and Neurorehabilitation Laboratory ARTORG Center for Biomedical Engineering Research University of Bern Switzerland Department of Cognitive Robotics Delft University of Technology Delft Netherlands Department Rehabilitation Medicine Erasmus Medical Center Rotterdam Netherlands

Whole-hand haptic rendering could lead to more naturalistic and intuitive virtual hand-object interactions, which could be especially beneficial for applications such as sensorimotor robotic neurorehabilitation. However, the majority of previously proposed whole-hand haptic rendering algorithms rely on effortful custom implementations or are not suited for the grounded haptic devices often used in neurorehabilitation. Therefore, we suggest a framework for whole-hand haptic rendering based on a readily available physics engine. We employ a bilateral position-position teleoperation framework between a haptic rehabilitation device and a simulated hand avatar with added exercise-specific haptic rendering. Moreover, in consideration of the needs of neurological patients, we introduce an adaptive damping of the haptic device during hand-object interactions for increased stabilization of the patient’s limb. We present the first results of the feasibility of the proposed framework in a haptic rehabilitation exercise. In an ongoing clinical study, the practical application of the presented framework is currently investigated.

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Gaussian Decay Function-based Improved Moment Matching for Ocular Artifacts Removal

Gaussian Decay Function-based Improved Moment Matching for O...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Jiang, Hua Shi, Qiuxia Liu, Siying Zhang, Jiuying Zhao, Qinglin Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering China Lanzhou University Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

Electroencephalogram (EEG) equipped with high time resolution that distracted by incoherent brain sources which including ocular artifacts (OAs) generating by blinks is of great significance. Therefore, these OAs got corrected before extracting information from EEG is indispensable. Improved Moment Matching (IMM) is a high-speed denoising algorithm suitable for removing OA in multi-channel EEG, which is an improvement of the moment matching method used to remove stripe noise in hyperspectral images. On foundation of this, this paper proposes an optimization algorithm for IMM based on Gaussian decay function (IMM_G). In the first place, the construction of the reference signal is optimized via utilizing a Gaussian decay function, thereby preventing the signal distortion caused by the filter. And then, a method for judging the to-be-processed interval is proposed to realizes the individualized processing of different channels. As a result, through quantitative comparison experiments with simulated data and real data from the UAIS laboratory, it was identified that IMM_G showed less time complexity, significantly enhanced arithmetic speed and denoising consequence while the detail retention ability of the original method for the non-blinking region of multi-channel EEG data is maintained. Hence, this method could be extensively used in High-density EEG (hdEEG) OAs removal scene. © 2022 IEEE.

关键词： Electroencephalography

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Effects of NGF-chitosan on alleviating secondary degeneration and repairing primary degeneration after expanded partial optic nerve transection

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Science China Life Sciences 2025年

作者： Xiao Liu Hongmei Duan Limin Gao Linhao Yuan Peng Hao Wen Zhao Yudan Gao Zitian Huang Xi Wang Wenjianlong Zhou Shunchang Ma Ningli Wang Kwok-Fai So Zhaoyang Yang Xiaoguang Li Wang Jia School of Engineering Medicine Beijing Key Laboratory for Biomaterials and Neural Regeneration Beihang University Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration Beijing Advanced Innovation Center for Big Data-Based Precision MedicineBeihang University China National Clinical Research Center for Neurological Diseases (NCRC-ND) Department of Neurosurgery Beijing Tiantan Hospital National Center for Neurological Disorders Capital Medical University Department of Neurobiology School of Basic Medical Sciences Capital Medical University Department of Neurotomy Beijing Neurosurgical Institute Capital Medical University Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Guangdong-Hongkong-Macau Institute of CNS Regeneration Ministry of Education CNS Regeneration Collaborative Joint Laboratory Jinan University Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) Department of Ophthalmology and State Key Laboratory of Brain and Cognitive Sciences The University of Hong Kong Center for Brain Science and Brain-Inspired Intelligence Guangdong-Hong Kong-Macao Greater Bay Area Co-innovation Center of Neuroregeneration Nantong University

Optic neuropathy is one of the main causes of irreversible blindness in the world, and there is no effective treatment in clinic. Both primary degeneration and secondary degeneration play an important role in the injury caused by optic neuropathy. Partial optic nerve transection(PONT) model can be used to study these two kinds of degeneration simultaneously. However, there is currently no measure that can effectively intervene in both types of injuries concurrently. Here, we constructed an expanded partial optic nerve transection(EPONT) model. Nerve growth factor(NGF)-chitosan locally implanted into the injured area could simultaneously intervene in the secondary and primary degeneration, not only protecting the ventral part of the injured optic nerve, but also promoting the regeneration of the dorsal part. Visual functions, including pupillary light reflex and depth perception, were also well preserved. NGF-chitosan exerted biological effects by enhancing the expression of NGF and tyrosine kinase A(Trk A) in the optic nerve and retinal ganglion cells(RGCs). Furthermore, NGFchitosan played a protective and repairing role by inhibiting the activation of microglia in the ventral area of the injured optic nerve and increasing the expression of mammalian target of rapamycin(m TOR) in RGCs. Our results demonstrate that the local use of NGF-chitosan in the injured area effectively repaired the optic nerve, which provides a new measure for the clinical treatment of optic nerve injury.

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