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Development of yellow-light TiO2 integrated photonics

Optics Letters 2025年第11期50卷 3489-3492页

作者： Chen, Zequn Tang, Yiheng Wei, Maoliang Li, Xiaojing Sun, Boshu Wu, Yingchun Zou, Sishuo Huang, Ji Si, Ke Gong, Wei Lin, Hongtao Li, Lan Institute of Advanced Technology Westlake Institute for Advanced Study Hangzhou310024 China Zhejiang Key Laboratory of 3D Micro Nano Fabrication and Characterization School of Engineering Westlake University Hangzhou310030 China The State Key Lab of Brain-Machine Intelligence College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China MOE Frontier Science Center for Brain Science & Brain-Machine Integration Zhejiang University Hangzhou310027 China Nanhu Brain-Computer Interface Institute Hangzhou311100 China College of Optical Science and Engineering Zhejiang University Hangzhou310027 China Westlake Institute for Optoelectronics Fuyang Zhejiang Hangzhou311421 China

Visible-light photonic integrated circuits (PICs) are rapidly emerging as a crucial technology to overcome the scaling challenges in quantum information processing and biosensing. Titanium dioxide (TiO2), with its high transparency in the visible range, is regarded as a promising material for facilitating the development of high-performance visible-light integrated systems. In this paper, we introduce a series of passive TiO2 integrated devices, which include waveguides, multimode interferometers (MMIs), and thermo-optic switches (TOSs), monolithically integrated for the first time to our knowledge at a working wavelength of 589 nm. A propagation loss of 5.46 ± 0.17 dB/cm demonstrates a significant improvement over previous reports on yellow-light TiO2 waveguides. This study marks the first fabrication of a TOS operating at 589 nm, featuring a TiO2 core and polymer cladding. Notably, it achieves a power consumption (Pπ) of 8.24 mW, which represents a threefold reduction compared to silicon nitride-based TOSs operating in the visible spectrum. These results offer new insight into the design and fabrication of visible waveguide-integrated devices, paving the way for future emerging applications, such as quantum information, sensors, and biomedical science. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Titanium dioxide

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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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A High-Performance Classification Method Based on CNN for Interventional EEG Data 7

A High-Performance Classification Method Based on CNN for In...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Li, Sining Liu, Gan Feng, Fan Wu, Dongdong Ma, Yudong Duan, Feng Nankai University Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China Tsinghua University Department of Biomedical Engineering School of Medicine Beijing China General Hospital of The People's Liberation Army Department of Neurosurgery Beijing China Air Force Medical University Department of Neurosurgery Beijing China

ISBN: (纸本)9798350363173

Interventional brain-computer interfaces (BCI) have been developed for many years and are capable of collecting electroencephalogram (EEG) signals. However, research on interventional EEG signals is just beginning. In this paper. We present a new deep learning network for intentional classification of interventional EEG in a monkey. Electrodes are placed on the inner walls of the vessels in the superior sagittal sinus to collect signals in four states. Then, we construct a Convolutional neural Networks (CNN) model to extract features and classify the states. In the experiments, we recognize the interventional EEG signals of the monkey in the four states, and the average recognition accuracy of its EEG signals in each state is more than 90% for the whole dataset, and 70% for the validation set. The experimental results show that the method in this paper can effectively recognize and classify interventional EEG signals in different states. Our work advances the application of interventional BCIs and paves the way for the future development of more versatile and responsive interventional EEG interfaces. © 2024 IEEE.

关键词： Electroencephalography

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Microstimulation-based path tracking control of pigeon robots through parameter adaptive strategy

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Heliyon 2024年第19期10卷 e38113页

作者： Huang, Yinggang Yang, Lifang Yang, Long Xu, Zehua Li, Mengmeng Shang, Zhigang School of Electrical and Information Engineering Zhengzhou University Zhengzhou 450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou 450001 China

Research on animal robots utilizing neural electrical stimulation is a significant focus within the field of neuro-control, though precise behavior control remains challenging. This study proposes a parameter-adaptive strategy to achieve accurate path tracking. First, the mapping relationship between neural electrical stimulation parameters and corresponding behavioral responses is comprehensively quantified. Next, adjustment rules related to the parameter-adaptive control strategy are established to dynamically generate different stimulation patterns. A parameter-adaptive path tracking control strategy (PAPTCS), based on fuzzy control principles, is designed for the precise path tracking tasks of pigeon robots in open environments. The results indicate that altering stimulation parameter levels significantly affects turning angles, with higher UPN and PTN inducing changes in the pigeons' motion state. In experimental scenarios, the average control efficiency of this system was 82.165%. This study provides a reference method for the precise control of pigeon robot behavior, contributing to research on accurate target path tracking. © 2024 The Author(s)

关键词： Behavioral pre-control Parameter adaptive strategy Path tracking control Pigeon robot Stimulus model

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Modeling and controller design of vertical take-off and landing turbojet powered aircraft

Modeling and controller design of vertical take-off and land...

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International Symposium on Autonomous Systems (ISAS)

作者： Tianyu Ouyang Yankai Yin Fei Kang Jiachen Zhang Yu Tian Xiaoquan Yan Wenchuan Kuang Feng Duan Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China Tianjin Fulibo Intelligent Technology Co. Ltd Tianjin China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

Aiming at the problems such as high altitude flight difficulty and poor wind resistance of rotary-wing UAV in disaster rescue, this paper proposes a vertical take-off and landing turbojet powered aircraft. The vehicle uses fuel as its power source, and four turbojet engines with vector propulsion systems provide thrust. Aiming at the difficult problem of controller design caused by complex nonlinear characteristics of aircraft, a T-S modeling method of aircraft is presented. Firstly, a dynamic model of the aircraft is established by analyzing the relationship between the force and rotation of the aircraft. On this basis, the T-S fuzzy model of aircraft is established by using sector nonlinear method. The model can decompose the complex nonlinear system into several simple linear subsystems, which is more convenient for controller design and analysis. To verify the validity of the T-S model, a fuzzy LQR controller is designed. Simulation results show that the designed controller can realize fast stabilization of the system. The structure design of the aircraft and T-S fuzzy control are of great practical significance for disaster relief in the complex plateau environment.

关键词： Resistance Analytical models Atmospheric modeling Disasters Vectors Aircraft manufacture Aircraft

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Instruction Cues Increase brain Network Complexity During Movement Preparation

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Journal of Shanghai Jiaotong university(Science) 2022年第2期27卷 202-210页

作者： WANG Ning ZHANG Lipeng ZHANG Rui MA Liuyang NIU Deyuan ZHANG Yankun ZHAO Hui HU Yuria The 27th Research Institute of China Electronics Technology Group Corporation Zhengzhou450047China School of Electrical Engineering Zhengzhou UniversityZhengzhou450001China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001China Zhengzhou Buen Technology Company Zhengzhou450001China Henan Institute of Metrology Zhengzhou450018China

Instruction cues are widely employed for research on neural mechanisms during movement ***,their influence on brain connectivity during movement has not received much ***,15 healthy subjects completed two experimental tasks including either instructed or voluntary movements;meanwhile electroencephalogram(EEG)data were synchronously *** on source analysis and related literature,six movement-related brain regions were selected,including the left/right supplementary motor area(SMA),left/right inferior frontal gyrus(iFg),and left/right postcentral gyrus(pCg).After assuming 10 a priori models of regional brain connectivity,we evaluated the optimal connectivity model between brain regions for the two scenarios using the dynamic causality model(DCM).During voluntary movement,the movement originated in the SMA,passed through the iFg of the prefrontal lobe,and then returned to the main sensory cortex of the *** the instructed movement,the movement originated in the iFg,and then was transmitted to the pCg and the SMA,as well as from the pCg to the *** contrast to the preparation process of voluntary movement,there were long-range information interactions between the iFg and ***,almost the same brain regions were active during movement preparation under both voluntary and instructed movement tasks,which evidences certain similarities in dynamic brain connectivity,that is,the brain has direct connections between the bilateral SMA,bilateral pCg,and bilateral SMA,indicating that the both brain hemispheres work together during the movement preparation *** results suggest that the network during the preparation process of instructed movements is more complex than voluntary movements.

关键词： electroencephalogram(EEG) dynamic causality model(DCM) voluntary movement instructed movement movement preparation

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EASING SEASICKNESS THROUGH ATTENTION REDIRECTION WITH A MINDFULNESS-BASED brain-computer interface

arXiv

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arXiv 2025年

作者： Bao, Xiaoyu Xu, Kailin Zhu, Jiawei Huang, Haiyun Li, Kangning Huang, Qiyun Li, Yuanqing School of Automation Science and Engineering South China University of Technology Guangzhou China Research Center for Brain-Computer Interface Pazhou Laboratory Guangzhou China School of Artificial Intelligence South China Normal University Foshan China School of Psychology South China Normal University Guangzhou China South China Brain-Computer Interface Technology Co. Ltd. Guangzhou China

Seasickness is a prevalent issue that adversely impacts both passenger experiences and the operational efficiency of maritime crews. While techniques that redirect attention have proven effective in alleviating motion sickness symptoms in terrestrial environments, applying similar strategies to manage seasickness poses unique challenges due to the prolonged and intense motion environment associated with maritime travel. In this study, we propose a mindfulness brain-computer interface (BCI), specifically designed to redirect attention with the aim of mitigating seasickness symptoms in real-world settings. Our system utilizes a single-channel headband to capture prefrontal electroencephalography (EEG) signals, which are then wirelessly transmitted to computing devices for the assessment of mindfulness states. The results are transferred into real-time feedback as mindfulness scores and audiovisual stimuli, facilitating a shift in attentional focus from physiological discomfort to mindfulness practices. A total of 43 individuals participated in a real-world maritime experiment consisted of three sessions: a real-feedback mindfulness session, a resting session, and a pseudofeedback mindfulness session. Notably, 81.39% of participants reported that the mindfulness BCI intervention was effective, and there was a significant reduction in the severity of seasickness, as measured by the Misery Scale (MISC). Furthermore, EEG analysis revealed a decrease in the theta/beta ratio, corresponding with the alleviation of seasickness symptoms. A decrease in overall EEG band power during the real-feedback mindfulness session suggests that the mindfulness BCI fosters a more tranquil and downregulated state of brain activity. Together, this study presents a novel nonpharmacological, portable, and effective approach for seasickness intervention, with the potential to enhance the cruising experience for both passengers and crews. © 2025, CC BY-NC-ND.

关键词： Electroencephalography

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Machine learning assisted screening of binary alloys for metal-based anode materials

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Journal of Energy Chemistry 2025年第5期104卷 62-68页

作者： Xingyue Shi Linming Zhou Yuhui Huang Chaohui Wu Yongjun Wu Juan Li Zijian Hong State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310058ZhejiangChina Nanhu Brain-Computer Interface Institute Hangzhou 311100ZhejiangChina Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Research Institute of Zhejiang University-TaizhouTaizhou 318000ZhejiangChina Inner Mongolia Electric Power Economic and Technological Research Institute Hohhot 010010Inner MongoliaChina College of Materials Science and Engineering Zhejiang University of TechnologyHangzhou 310014ZhejiangChina School of Engineering Hangzhou City UniversityHangzhou 310015ZhejiangChina Institute of Fundamental and Transdisciplinary Research Zhejiang UniversityHangzhou 311121China

Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite ***,experimental investigations of metal alloys can be time-consuming and expensive,often requiring extensive experimental design and *** this study,we developed a machine learning model based on the Crystal Graph Convolutional neural Network(CGCNN)to screen alloy anode materials for seven battery systems,including lithium(Li),sodium(Na),potassium(K),zinc(Zn),magnesium(Mg),calcium(Ca),and aluminum(Al).We utilized data with tens of thousands of alloy materials from the Materials Project(MP)and Automatic FLOW for Materials Discovery(AFLOW)*** any experimental voltage input,we identified over 30 alloy systems that have been experimentally validated with good ***,we predicted over 100 alloy anodes with low potential and high specific *** hope this work to spur further interest in employing advanced machine learning models for the design of battery materials.

关键词： CGCNN Alloy anodes Machine learning Metal-based batteries

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A Portable and Wearable Visual brain-computer interface for Assessing Patients with Disorders of Consciousness 3

A Portable and Wearable Visual Brain-Computer Interface for ...

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3rd International Conference on Electronic Information Engineering and computer Communication, EIECC 2023

作者： Wu, Huang Xiao, Jun Xue, Chenxin Chen, Di Pan, Jiahui Li, Yuanqing School of Automation Science and Engineering South China University of Technology Guangzhou China School of Electric Power Engineering South China University of Technology Guangzhou China School of Software South China Normal University Guangzhou China Research Center for Brain-Computer Interface Pazhou Laboratory Guangzhou China

ISBN: (纸本)9798350359961

The JFK Coma Recovery Scale-Revised (JFK CRSR) is a clinical behavioral method for assessing patients with disorders of consciousness (DOC). However, due to severe brain injury, the DOC patients may lack sufficient behavioral responses, leading to a relatively high clinical misdiagnosis rate of the JFK CRS-R. To address this limitation, this study developed a portable and wearable brain-computer interface (BCI) to evaluate the visual tracking abilities of the DOC patients and enhance the diagnostic accuracy of the JFK CRS-R. During the experiment, patients wore an Electroencephalogram headband, and the Graphical User interface of the BCI presented four human images with a background captured by a camera. A target human image was randomly selected, then the four images alternately flashed until the target moved to its initial position. The system guided patients to concentrate their attention on the moving target. In a case study, the BCI system was applied to a patient diagnosed with a minimally conscious state (MCS). In contrast to the lack of visual tracking behavior in CRS-R, our experiment result showed a significant accuracy of 60% achieved by the MCS patient, emphasizing the visual tracking abilities of the MCS patients and highlighting the effectiveness of the system. © 2023 IEEE.

关键词： brain-computer interface Disorders of Consciousness JFK Coma Recovery ScaleRevised Visual Tracking

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Enhanced Assessment of Auditory Perception Function in Disorders of Consciousness Using a Minimalist Wearable brain-computer interface 4

Enhanced Assessment of Auditory Perception Function in Disor...

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4th International Conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Xue, Chenxin Xiao, Jun Wu, Huang Hu, Li Pan, Jiahui Li, Yuanqing South China University of Technology School of Automation Science and Engineering Guangzhou China South China University of Technology School of Electric Power Engineering Guangzhou China South China Normal University School of Software Guangzhou China Pazhou Laboratory Research Center for Brain-Computer Interface Guangzhou China

ISBN: (纸本)9798350373233

The Coma Recovery Scale-Revised (CRS-R) is a clinical behavioral method for assessing the levels of consciousness and perceptual abilities in patients with Disorders of Consciousness (DOC). However, due to severe impairment of motor functions in DOC patients and subjective factors of clinical evaluators, the misdiagnosis rate of the CRS-R is very high. brain-computer interfaces (BCIs) can directly detect patients' brain activity in response to external stimuli, independent of patients' behavioral representation, thereby achieving a more objective assessment. In this study, we introduced a minimalist wearable BCI system grounded in the auditory oddball paradigm to enhance assessment of auditory perception function in DOC patients. In the experiments, sinusoidal tones and the complex sound of clapping were selected as standard stimuli occurring frequently and occasional deviant stimuli, respectively. The specific event-related potentials evoked by the auditory stimuli facilitated the assessment of auditory perceptual function. The experimental results indicate that the DOC patients with auditory perception functions can also be detected by the BCI system. As a result, the BCI could serve as a valuable adjunct tool in clinical practice of the CRS-R. © 2023 IEEE.

关键词： brain computer interface

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