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IEEE International Joint Conference on Biometrics (IJCB)

作者： Zhiping Lai Xiaoyang Kang Hongbo Wang Xueze Zhang Weiqi Zhang Fuhao Wang Institute of AI and Robotics Academy for Engineering & Technology Fudan University China Engineering Research Center of AI & Robotics Ministry of Education China Shanghai Engineering Research Center of AI & Robotics China MOE Frontiers Center for Brain Science China State Key Laboratory of Medical Neurobiology China Yiwu Research Institute of Fudan University China Ji Hua Laboratory China Research Center for Intelligent Sensing Zhejiang Lab China Shanghai Robot Industrial Technology Research Institute China Shanghai Clinical Research Center for Aging and Medicine China

ISBN: (纸本)9781665463959

Gesture recognition using surface electromyography (sEMG) shows its great potential in the field of human-computer interaction (HCI). Previous works achieve relatively good performance based on the assumption of invariant statistic distribution. However, the practical application effect is unsatisfactory due to the problem of domain shift. Existing approaches need plenty of labeled sEMG samples from target scenarios for calibration, which is burdensome for experimenters and users. In this work, we present a contrastive self-supervised learning framework (ConSSL) for sEMG-based gesture recognition to realize domain adaptation in target domains. After pretraining on a bunch of unlabeled samples, only a small number of labeled samples are needed for calibration and domain adaptation. Experimental results indicate that the proposed framework out-performs other approaches even $if\leq 50\%$ labeled samples in target scenarios are available and achieves the state-of-the-art.

关键词： Human computer interaction Electric potential Biometrics (access control) Gesture recognition Self-supervised learning Electromyography Calibration

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Optimal Landing of Tilt-rotor Aircraft after Engine Failure Considering Pilot Inherent Limitations

Optimal Landing of Tilt-rotor Aircraft after Engine Failure ...

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IEEE International Conference on robotics and Biomimetics

作者： Xufei Yan Renliang Chen Shiqiang Zhu Anhuan Xie Jason Gu Research Center for Intelligent Robotics Zhejiang Lab Hangzhou 311100 China. Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China National Key Laboratory of Science and Technology on Rotorcraft Aeromechanics College of Aeronautics Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China Intelligent Robot Research Center Zhejiang Lab Hangzhou Zhejiang China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

An augmented longitudinal rigid-body model is developed with a set of algebra equations describing the controls in the cockpit and the differential equations describing the pilot inherent limitations. The landing procedure after one engine failure is formulated into a nonlinear optimal control problem. XV-15 tilt-rotor aircraft is taken as the sample for the demonstration of landing in one engine failure during short takeoff. The results show that the total power required, thrust coefficient, longitudinal flapping angle and optimal controls are more relatively gentle than the solutions without considering the pilot inherent limitations. Compared with the basic longitudinal rigid-body model, the optimal solutions involve more control information.

关键词： Atmospheric modeling Optimal control Rotors Mathematical models History Aircraft propulsion Aircraft

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Learning compliant grasping and manipulation by teleoperation with adaptive force control

arXiv

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arXiv 2021年

作者： Zeng, Chao Li, Shuang Jiang, Yiming Li, Qiang Chen, Zhaopeng Yang, Chenguang Zhang, Jianwei Department of Informatics Universiẗat Hamburg Germany National Engineering Laboratory for Robot Visual Perception and Control Hunan University China Center for Cognitive Interaction Technology Bielefeld University Germany Bristol Robotics Laboratory University of the West of England United Kingdom

In this work, we focus on improving the robot's dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framework, is exploited to map human hand postures to a multifingered robot hand. We augment TeachNet, which is originally based on an imprecise kinematic mapping and position-only servoing, with a biomimetic learning-based compliance control algorithm for dexterous manipulation tasks. This compliance controller takes the mapped robotic joint angles from TeachNet as the desired goal, computes the desired joint torques. It is derived from a computational model of the biomimetic control strategy in human motor learning, which allows adapting the control variables (impedance and feedforward force) online during the execution of the reference joint angle trajectories. The simultaneous adaptation of the impedance and feedforward profiles enables the robot to interact with the environment in a compliant manner. Our approach has been verified in multiple tasks in physics simulation, i.e., grasping, opening-a-door, turning-a-cap, and touching-a-mouse, and has shown more reliable performances than the existing position control and the fixed-gain-based force control approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Remote control

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Dysfunctional resting-state EEG microstate correlated with the severity of cigarette exposure in nicotine addiction

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Science China(Information Sciences) 2020年第7期63卷 66-68页

作者： Yan CHENG Junjie BU Nan LI Jian LI Huixing GOU Shinan SUN Chang LIU Zida JIN Changle HE Chuan FAN Chialun LIU Xiaochu ZHANG School of Humanities and Social Science University of Science and Technology of China School of Biomedical Engineering Research and Engineering Center of Biomedical MaterialsAnhui Medical University Lab for Cognitive Brain Mapping RIKEN Center for Brain Science Eye Center Department of Ophthalmology the First Affiliated Hospital of USTC Hefei National Laboratory for Physical Sciences at the Microscale School of Life Sciences Division of Life Sciences and Medicine University of Science and Technology of China Academy of Psychology and Behavior Tianjin Normal University Department of Medical Psychology the First Affiliated Hospital of Anhui Medical University

Dear editor,Nicotine is a significant cause of severe physical and psychiatric disorders. Evidence suggests that smokers have poorer global cognitive functioning in later life, as well as lower average scores in several cognitive domains, including cognitive flexibility and memory [1]. There are large numbers of electroencephalography (EEG) such as event-

关键词： nicotine addiction resting-state EEG microstate microstate duration pack-years FTND

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An Untethered Soft robotic Gripper with Adjustable Grasping Modes and Force Feedback

An Untethered Soft Robotic Gripper with Adjustable Grasping ...

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IEEE International Conference on robotics and Biomimetics

作者： Yang Yang Honghui Zhu Jia Liu Yingtian Li Jianshu Zhou Tao Ren Yi Ren School of Automation Nanjing University of Information Science and Technology Nanjing Jiangsu China Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu China Advanced Manufacturing Lab Huawei Technologies Shenzhen China

ISBN: (纸本)9781665481106

In this paper, we propose a three-finger soft robotic gripper, which adopts an untethered and under-actuated design with simple structure and easy operation. The rotation of two servomotors is used to control the positions of the fingers, which provides the fingers with adjustable grasping modes and improves the adaptation of the gripper to objects with different shapes. A pressure sensor is integrated at the end of the finger to provide force feedback, which is used to detect the contact force when grasping the object and enables the gripper to provide the proper grasping force. The experimental section characterizes the bending of the soft robotic finger, and the gripper is integrated at the end of a collaborative robotic arm to demonstrate its adjustable grasping posture and force feedback function. The soft robotic gripper is expected to be used in a variety of scenarios, such as service robot, food industry or garbage sorting.

关键词： Service robots Shape Fingers Force feedback Force Grasping Soft robotics

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Detecting Brain State Changes via Manifold Mean Shifting

Detecting Brain State Changes via Manifold Mean Shifting

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Huang, Zhuobin Dan, Tingting Lin, Yi Chen, Jiazhou Cai, Hongmin Wu, Guorong South China University of Technology School of Computer Science and Engineering Guangzhou China Brain and Affective Cognitive Research Center Pazhou Lab Guangzhou China University of North Carolina at Chapel Hill Department of Psychiatry Chapel HillNC United States Department of Computer Science University of North Carolina at Chapel Hill Chapel HillNC United States

ISBN: (纸本)9781665401265

The topology of human functional networks is assumed to oscillate during brain states changes. The functional neuroimage is employed to offer a non-invasive window to understand cognition and behaviors by characterizing the functional connections between spatially distinct brain regions. Consequently, identifying the transitions of functional connectivities is the critical step to understanding the mechanism of cognition that might be underlined with neurological disorders. However, little attention has been paid to studying the geometry of the entire functional brain network. To tackle this issue, this paper models the cognition changes on functional brain networks as a set of landmarks residing on a Riemannian manifold. Accordingly, we propose a Riemannian manifold mean shift method to detect cognition changes by identifying the representative function networks of the distribution of functional networks. The manifold mean shift (MMS) method is applied on both simulated data and real functional neuroimaging data, downloaded from Human Connectome Project (HCP). Experimental results demonstrated the MMS achieved highly accurate and consistent cognition change, by comparing three state-of-the-art methods. © 2021 IEEE.

关键词： Geometry

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Neural population geometry reveals the role of stochasticity in robust perception 21

Neural population geometry reveals the role of stochasticity...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Joel Dapello Jenelle Feather Hang Le Tiago Marques David D. Cox Josh H. McDermott James J. DiCarlo Sue Yeon Chung Department of Brain and Cognitive Sciences Massachusetts Institute of Technology and McGovern Institute for Brain Research Massachusetts Institute of Technology and School of Engineering and Applied Sciences Harvard University Department of Brain and Cognitive Sciences Massachusetts Institute of Technology and McGovern Institute for Brain Research Massachusetts Institute of Technology and Center for Brains Minds and Machines Massachusetts Institute of Technology Department of Brain and Cognitive Sciences Massachusetts Institute of Technology MIT-IBM Watson AI Lab Department of Brain and Cognitive Sciences Massachusetts Institute of Technology and McGovern Institute for Brain Research Massachusetts Institute of Technology and Center for Brains Minds and Machines Massachusetts Institute of Technology and Speech and Hearing Bioscience and Technology Harvard University Department of Brain and Cognitive Sciences Massachusetts Institute of Technology and Center for Theoretical Neuroscience Columbia University and Zuckerman Institute Columbia Univeristy

ISBN: (纸本)9781713845393

Adversarial examples are often cited by neuroscientists and machine learning researchers as an example of how computational models diverge from biological sensory systems. Recent work has proposed adding biologically-inspired components to visual neural networks as a way to improve their adversarial robustness. One surprisingly effective component for reducing adversarial vulnerability is response stochasticity, like that exhibited by biological neurons. Here, using recently developed geometrical techniques from computational neuroscience, we investigate how adversarial perturbations influence the internal representations of standard, adversarially trained, and biologically-inspired stochastic networks. We find distinct geometric signatures for each type of network, revealing different mechanisms for achieving robust representations. Next, we generalize these results to the auditory domain, showing that neural stochasticity also makes auditory models more robust to adversarial perturbations. Geometric analysis of the stochastic networks reveals overlap between representations of clean and adversarially perturbed stimuli, and quantitatively demonstrates that competing geometric effects of stochasticity mediate a tradeoff between adversarial and clean performance. Our results shed light on the strategies of robust perception utilized by adversarially trained and stochastic networks, and help explain how stochasticity may be beneficial to machine and biological computation.1

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Reler: Relearning Controversial Regions to Accurately Segment Nasopharyngeal Carcinoma

Reler: Relearning Controversial Regions to Accurately Segmen...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Guihua Tao Haojiang Li Dandan Lu Ziqin Ling Lizhi Liu Hongmin Cai School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat-sen University Cancer Center Sun Yat-sen University Guangzhou China Brain and Affective Cognitive Research Center Pazhou Lab Guangzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Accurate nasopharyngeal carcinoma (NPC) segmentation is significant in preventing local recurrence and improving patients’ survival rates. However, existing deep learning-based methods often yield unsatisfactory segmentation results, especially in fine-grained detail. Because NPC is a tiny and infiltrative tumor with a huge background, traditional deep neural networks tend to be dominated by salient information, thus missing the fine-grained details of NPC. To achieve accurate NPC segmentation, a relearning controversial regions method (Reler) is proposed. It consists of three modules, including the controversial features generator (CFG), controversial features finding module (CFF), and controversial regions arbitration module (CRA). First, CFG constructs global and local feature extractors to generate two types of different features. Then, CFF finds the controversial features and corresponding regions by comparing the global and local features’ estimates of the segmentation results of the same input regions. Next, the CRA focuses on controversial features, relearns new features, and produces new segmentation results through a proposed Transformer-based self-attention network. Finally, the uncontroversial segmentation results from CFF and CRA are combined as the final segmentation results. Extensive experiments are conducted on a large NPC dataset containing 6342 images from 596 patients. The experimental results show that the proposed method Reler is effective and superior to the nine state-of-the-art methods.

关键词： Learning systems Deep learning Image segmentation Neural networks Feature extraction Transformers Generators

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Exploring Video-Based Driver Activity Recognition under Noisy labels

arXiv

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arXiv 2025年

作者： Fan, Linjuan Wen, Di Peng, Kunyu Yang, Kailun Zhang, Jiaming Liu, Ruiping Chen, Yufan Zheng, Junwei Wu, Jiamin Han, Xudong Stiefelhagen, Rainer Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany School of Robotics National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China Shanghai AI Lab China University of Sussex United Kingdom

As an open research topic in the field of deep learning, learning with noisy labels has attracted much attention and grown rapidly over the past ten years. Learning with label noise is crucial for driver distraction behavior recognition, as real-world video data often contains mislabeled samples, impacting model reliability and performance. However, label noise learning is barely explored in the driver activity recognition field. In this paper, we propose the first label noise learning approach for the driver activity recognition task. Based on the cluster assumption, we initially enable the model to learn clustering-friendly low-dimensional representations from given videos and assign the resultant embeddings into clusters. We subsequently perform co-refinement within each cluster to smooth the classifier outputs. Furthermore, we propose a flexible sample selection strategy that combines two selection criteria without relying on any hyperparameters to filter clean samples from the training dataset. We also incorporate a self-adaptive parameter into the sample selection process to enforce balancing across classes. A comprehensive variety of experiments on the public Drive&Act dataset for all granularity levels demonstrates the superior performance of our method in comparison with other label-denoising methods derived from the image classification field. The source code is available at https://***/ilonafan/DAR-noisy-labels. Copyright © 2025, The Authors. All rights reserved.

关键词： Object recognition

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Computational neuroscience:a frontier of the 21st century

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National Science Review 2020年第9期7卷 1418-1422页

作者： Xiao-Jing Wang Hailan Hu Chengcheng Huang Henry Kennedy Chengyu Tony Li Nikos Logothetis Zhong-Lin Lu Qingming Luo Mu-ming Poo Doris Tsao Si Wu Zhaohui Wu Xu Zhang Douglas Zhou Center for Neural Science New York University Center for Neuroscience Key Laboratory of Medical Neurobiology of the Ministry of Health of China School of Medicine Zhejiang University Department of Neuroscience and Department of Mathematics Center for the Neural Basis of Cognition University of Pittsburgh Université Claude Bernard Lyon 1 Inserm Stem Cell and Brain Research Institute U1208 Institute of Neuroscience State Key Laboratory of Neuroscience Chinese Academy of SciencesCAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Center for Brain Science and Brain-Inspired Technology Division of Arts and Sciences and NYU-ECNU Institute of Cognitive Neuroscience NYU Shanghai School of Biomedical Engineering Hainan University Huazhong University of Science and Technology-Suzhou Institute for Brainsmatics Division of Biology and Biological Engineering California Institute of Technology Howard Hughes Medical Institute School of Electronics Engineering and Computer Science IDG/Mc Govern Institute for Brain Research PKU-Tsinghua Center for Life SciencesPeking University College of Computer Science and Technology Zhejiang University Institute of Brain-Intelligence Science and Technology Zhangjiang Lab School of Mathematical Sciences MOE-LSCand Institute of Natural Sciences Shanghai Jiao Tong University

THEORY IN NEUROSCIENCE The human brain is a biological organ,weighing about three pounds or 1.4 kg,that determines our behaviors, thoughts,emotions and consciousness. Although comprising only 2%of the total body weigh...

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