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2024 International Conference on Optoelectronic Information and computer Engineering, OICE 2024

作者： Liu, Lusheng Le, Song Zhang, Yuan Zhang, Jing Wang, Aiping Bin, Jianxiong Niu, Lan Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering & Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9781510682504

Optogenetics technology has greatly promoted the development of neuroscience. Flexible gene manipulation tools and sensitive light activation methods have provided convenience for neural function research. However, the thermal effect generated in the process of light stimulation affects the activation of neurons by optogenetic technology, which makes the results of light-activated light-sensitive proteins to generate neural currents inaccurate. Here, we established an in vivo activation model of LED-based optogenetic technology through simulation and analyzed the thermal effect produced by it. We propose to use microchannels to dissipate heat from implanted devices, reducing the thermal effect of light stimulation for neural activation. Through simulation, we propose the use of the vortex-like and cobweb-like microchannels for heat dissipation, which can control the temperature rise within 0.28K and 0.31K, respectively. © 2024 SPIE.

关键词： Heat losses

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Deep Audio-visual Learning:A Survey

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International Journal of Automation and computing 2021年第3期18卷 351-376页

作者： Hao Zhu Man-Di Luo Rui Wang Ai-Hua Zheng Ran He Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and TechnologyAnhui UniversityHefei 230601China Center for Research on Intelligent Perception and Computing(CRIPAC)and National Laboratory of Pattern Recognition(NLPR) Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of the Chinese Academy of SciencesBeijing 100049China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Audio-visual learning,aimed at exploiting the relationship between audio and visual modalities,has drawn considerable attention since deep learning started to be used *** tend to leverage these two modalities to improve the performance of previously considered single-modality tasks or address new challenging *** this paper,we provide a comprehensive survey of recent audio-visual learning *** divide the current audio-visual learning tasks into four different subfields:audiovisual separation and localization,audio-visual correspondence learning,audio-visual generation,and audio-visual representation ***-of-the-art methods,as well as the remaining challenges of each subfield,are further ***,we summarize the commonly used datasets and challenges.

关键词： Deep audio-visual learning audio-visual separation and localization correspondence learning generative models representation learning

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A Teleoperated Manipulator Control System Based on Surface Electromyography Using Deep Learning 12

A Teleoperated Manipulator Control System Based on Surface E...

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12th International Conference on CYBER technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Lai, Zhiping Zhang, Xueze Wang, Junkongshuai Mu, Wei Wang, Aiping Niu, Lan Zhang, Lihua Wang, Hongbo Kang, Xiaoyang Bin, Jianxiong Shanghai Engineering Research Center of Ai and Robotics Moe Frontiers Center for Brain Science Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Laboratory for Neural Interface and Brain Computer Interface State Key Laboratory of Medical Neurobiology Engineering Research Center of Ai and Robotics Ministry of Education Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Gesture recognition based on surface electromyography (sEMG) is a popular topic in teleoperation field, which has the characteristics of convenience, maneuverability, flexibility and adaptability. With the continuous development of deep learning technology, the variety and accuracy of gesture recognition are constantly improving. In our previous research, a new neural network based on graph convolutional network and temporal convolutional network (STCN-GR) was proposed and achieved good results in gesture recognition task. In this paper, we develop a teleoperated manipulator control system based on STCN-GR combined with majority voting. The experimental results show that the performance of proposed system can achieve relatively excellent performance in recognition accuracy and computation speed. In this paper, we choose a mechanism hand as a peripheral to test the performance of the STCN-GR based control system, and the results show that this system can be applied in teleoperation in feature work to provide convenience for human life. © 2022 IEEE.

关键词： Remote control

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Evolutionary Dynamic Optimization-Based Calibration Framework for Agent-Based Financial Market Simulators 13

Evolutionary Dynamic Optimization-Based Calibration Framewor...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Yang, Zhenhua Zhong, Muyao Yang, Peng Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Statistics and Data Science Shenzhen518055 China

ISBN: (纸本)9798350308365

The agent-based financial market simulators serve as an important validation tool for trading strategies. For high-fidelity simulation, it is pivotal to calibrate the parameters of a simulator so that the generated simulation data resembles the observed real market data of interest. In traditional calibration methods, it is typical that the parameters of the simulator are set to be time-invariant. However, the dynamic nature of the real financial market introduces various variability into the behaviors of the market participants over different time intervals, posing in-herent limitations to the traditional methods. A more reasonable approach might involve employing a simulator with time-variant parameters. This suggests that the model parameters can be dynamically adjusted at different stages of the simulation to adapt to the evolving market. Consequently, the calibration problem of the financial market simulators can be treated as a dynamic optimization problem. To dynamically calibrate the simulators, we introduce an Evolutionary Dynamic Optimization (EDO) framework. By monitoring the changes of the best fitness, the whole simulation time interval is adaptively divided into multiple stages. Then the Negatively Correlated Search (NCS) algorithm is employed to effectively adjust the parameters at different simulation stages to better simulate the real financial market. Empirical results on both synthetic and real data verify that our dynamic calibration framework significantly outperforms traditional calibration methods that fixing a parameter for the whole simulation interval. The proposed strategy of detecting dynamic changes is also shown to be more reliable than the naive method of manually segmenting stages. In terms of calibration time, our proposed method significantly improves by nearly 93% compared to the fixed parameter setting, and approximately 61% compared to manual segmentation calibration. © 2024 IEEE.

关键词： Financial markets

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An allocation method of crowdsourcing tasks for protecting fairness of participants 23

An allocation method of crowdsourcing tasks for protecting f...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Xiao, Lei Gao, Zhigang Xu, Ruichao Wu, Bo Zheng, Leilei Zhao, Wei Cen, Weipeng He, Xuanzhang College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province Hangzhou China School of Computer Science Tokyo University of Technology Hachioji Japan Interdisciplinary Graduate School University of Yamanashi Kofu400-8511 Japan

ISBN: (纸本)9781665494571

With the increasingly powerful functions of mobile devices and the popularity of wireless networks, spatial crowdsourcing has become a new service model. This paper studies the task assignment problem in spatial crowdsourcing, which reduces the commuting cost of participants while ensuring the fairness of participants under the condition of task execution order constraints. This paper first proposes the Fairness Monte Carlo Tree Search (FMCTS) task allocation algorithm, ensuring that the maximum attendance difference of participants is below the set value while reducing the participants' commuting costs. After that, to optimize the efficiency of the algorithm, a FMCTS-Guided (FMCTSG) task allocation algorithm is proposed, which can reduce the running time of the algorithm. Finally, for non-emergency tasks, a Delayed FMCTS (DFMCTS) algorithm is proposed, which can further reduce the commuting cost of task participants. The experimental results show that the task allocation method proposed in this paper can effectively reduce the commuting cost of participants while ensuring the fairness of participants. © 2021 IEEE.

关键词： Crowdsourcing Costs Monte Carlo methods Smart cities Wireless networks Mobile handsets Resource management

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Instance-Aware Diffusion Implicit Process for Box-Based Instance Segmentation 26

Instance-Aware Diffusion Implicit Process for Box-Based Inst...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Ren, Hao Liu, Xingsong Huang, Junjian Wan, Ru Pu, Jian Lu, Hong Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China Institute of Science and Technology for Brain-inspired Intelligence Fudan University China Mogo.ai Information and Technology Co. Ltd. Shanghai China

ISBN: (纸本)9781643684369

The diffusion model has demonstrated impressive performance in image generation, but its potential for discriminative tasks such as instance segmentation remains unexplored. In this paper, we propose an Instance-aware Diffusion Implicit Process (IDIP) framework for instance segmentation based on boxes. During training, IDIP diffuses ground-truth boxes across various time steps, extracting corresponding Region of Interest (RoI) features. Dynamic convolution is then used to predict boxes and categories for each RoI, and the mask head generates masks from these predictions. During inference, IDIP iteratively refines randomly generated boxes with the denoising diffusion implicit model, while the mask head derives final masks from RoIs based on the refined boxes. Our method surpasses existing approaches on the COCO benchmark, requiring fewer training steps and less memory resources due to its dynamic design and instance-aware characteristic. © 2023 The Authors.

关键词： Diffusion

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Prognosis for patients with cognitive motor dissociation identified by brain-computer interface (vol 143, pg 1177, 2020)

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brain 2020年第8期143卷 1177-1189页

作者： Pan, Jiahui Xie, Qiuyou Qin, Pengmin Chen, Yan He, Yanbin Huang, Haiyun Wang, Fei Ni, Xiaoxiao Cichocki, Andrzej Yu, Ronghao Li, Yuanqing Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China School of Software South China Normal University Guangzhou China Department of Rehabilitation Medicine Zhujiang Hospital Southern Medical University Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Centre for Studies of Psychological Applications Guangdong Key Laboratory of Mental Health and Cognitive Science School of Psychology South China Normal University Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Department of Traumatic Brain Injury Rehabilitation and Severe Rehabilitation Guangdong Work Injury Rehabilitation Hospital Guangzhou China Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China School of Software South China Normal University Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Skolkovo Institute of Science and Technology (Skoltech) Moscow 143026 Russia Nicolaus Copernicus University (UMK) Torun 87-100 Poland Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China Correspondence to: Yuanqing Li PhD Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou 510640 China E-mail: auyqli@scut.edu.c

Cognitive motor dissociation describes a subset of patients with disorders of consciousness who show neuroimaging evidence of consciousness but no detectable command-following behaviours. Although essential for family counselling, decision-making, and the design of rehabilitation programmes, the prognosis for patients with cognitive motor dissociation remains under-investigated. The current study included 78 patients with disorders of consciousness who showed no detectable command-following behaviours. These patients included 45 patients with unresponsive wakefulness syndrome and 33 patients in a minimally conscious state, as diagnosed using the Coma Recovery Scale-Revised. Each patient underwent an EEG-based brain-computer interface experiment, in which he or she was instructed to perform an item-selection task (i.e. select a photograph or a number from two candidates). Patients who achieved statistically significant brain-computer interface accuracies were identified as cognitive motor dissociation. Two evaluations using the Coma Recovery Scale-Revised, one before the experiment and the other 3 months later, were carried out to measure the patients’ behavioural improvements. Among the 78 patients with disorders of consciousness, our results showed that within the unresponsive wakefulness syndrome patient group, 15 of 18 patients with cognitive motor dissociation (83.33%) regained consciousness, while only five of the other 27 unresponsive wakefulness syndrome patients without significant brain-computer interface accuracies (18.52%) regained consciousness. Furthermore, within the minimally conscious state patient group, 14 of 16 patients with cognitive motor dissociation (87.5%) showed improvements in their Coma Recovery Scale-Revised scores, whereas only four of the other 17 minimally conscious state patients without significant brain-computer interface accuracies (23.53%) had improved Coma Recovery Scale-Revised scores. Our results suggest that patients with cogn

关键词： cognitive motor dissociation brain-computer interface disorders of consciousness prognosis unresponsive wakefulness syndrome

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Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification

arXiv

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arXiv 2020年

作者： Liu, Gang Pang, Yajing Yin, Shuai Niu, Xiaoke Wang, Jing Wan, Hong School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Institute of Robotics and Intelligent Systems Xi'an Jiaotong University Xi'An710049 China Henan Provincial Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China

Nonlinear mapping is an essential and common demand in online systems, such as sensor systems and mobile phones. Accelerating nonlinear mapping will directly speed up online systems. Previously the authors of this paper proposed a Dendrite Net (DD) with enormously lower time complexity than the existing nonlinear mapping algorithms;however, there still are redundant calculations in DD. This paper presents a DD with an acceleration module (AC) to accelerate nonlinear mapping further. We conduct three experiments to verify whether DD with AC has lower time complexity while retaining DD's nonlinear mapping properties and system identification properties: The first experiment is the precision and identification of unary nonlinear mapping, reflecting the calculation performance using DD with AC for basic functions in online systems. The second experiment is the mapping precision and identification of the multi-input nonlinear system, reflecting the performance for designing online systems via DD with AC. Finally, this paper compares the time complexity of DD and DD with AC and analyzes the theoretical reasons through repeated experiments. Results: DD with AC retains DD's excellent mapping and identification properties and has lower time complexity. Significance: DD with AC can be used for most engineering systems, such as sensor systems, and will speed up computation in these online systems. The code of DD with AC is available on GitHub:Gang neuron. © 2020, CC BY-NC-SA.

关键词： Online systems

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TSOM: Small Object Motion Detection Neural Network Inspired by Avian Visual Circuit

arXiv

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arXiv 2024年

作者： Hu, Pingge Zhang, Xiaoteng Li, Mengmeng Zhu, Yingjie Shi, Li Department of Automation Tsinghua University Beijing100084 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China Shenzhen Key Laboratory of Drug Addiction Shenzhen Neher Neural Plasticity Laboratory Brain Cognition and Brain Disease Institute Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an inspiration from nature, the avian visual system is capable of processing motion information in various complex aerial scenes, and its Retina-OTRt visual circuit is highly sensitive to capturing the motion information of small objects from high altitudes. However, more needs to be done on small object motion detection algorithms based on the avian visual system. In this paper, we conducted mathematical modeling based on extensive studies of the biological mechanisms of the Retina-OT-Rt visual circuit. Based on this, we proposed a novel tectum small object motion detection neural network (TSOM). The neural network includes the retina, SGC dendritic, SGC Soma, and Rt layers, each layer corresponding to neurons in the visual pathway. The Retina layer is responsible for accurately projecting input content, the SGC dendritic layer perceives and encodes spatial-temporal information, the SGC Soma layer computes complex motion information and extracts small objects, and the Rt layer integrates and decodes motion information from multiple directions to determine the position of small objects. Extensive experiments on pigeon neurophysiological experiments and image sequence data showed that the TSOM is biologically interpretable and effective in extracting reliable small object motion features from complex high-altitude backgrounds. Copyright © 2024, The Authors. All rights reserved.

关键词： Timing circuits

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An on-line BCI system for hand movement control using real-time recurrent probabilistic neural network

An on-line BCI system for hand movement control using real-t...

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International IEEE/EMBS Conference on Neural Engineering, CNE

作者： Mohammad Ahmadi Abbas Erfanian Brain-Computer Interface Laboratory Neural Technology Research Center Department of Biomedical Engineering Iran University of Science and Technology Tehran Iran

This paper presents a new EEG-based brain-computer interface (BCI) for on-line controlling the hand movement in a virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. For this purpose, two classifiers are designed. The first classifier which is based on the imagination of right-hand movement is for controlling the hand grasping, holding and opening. The second classifier, which is based on the imagination of left-hand movement is designed for error correction and activating the first classifier. One important issue in developing an on-line BCI is the robust and accurate classification of EEG signal which is characterized with a time-varying distribution. In this work, we present a real-time recurrent probabilistic neural network for classifying the EEG signals. The results show that the subjects were able to achieve an accuracy more than 80% during the first session of experiment without off-line training and 73%-91% during the last session using single-trial classification with no adaptation.

关键词： Control systems Real time systems Neural networks Recurrent neural networks Biological neural networks Electroencephalography brain computer interfaces Virtual reality Grasping Error correction

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