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Online Calibration of Intracortical Neural Interface Based on Transfer Learning

Journal of Physics: Conference Series 2018年第1期1069卷

作者： Bo Peng Wei Li Jiping He School of Automation Huazhong University of Science and Technology Wuhan 430074 P. R. China Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Wuhan 430074 P. R. China Department of Mechanical Engineering Beijing Institute of Technology Beijing 100081 P. R. China

In the application of neural interface, the neural activity of neurons and neuronal groups is not fixed even under the same task conditions. Meanwhile, the recording conditions of neural signals are also very unstable, with a high degree of within-and across-day variability. This results in a very unstable firing pattern for the recorded neural spike signals. In order to get better performance, the decoder often requires a lot of online calibration samples. This brings a heavy training burden to neural interface users. To solve this problem, this paper proposes to apply transfer learning (TL) to online calibration of intracortical neural interface to reduce the dependence of decoder on a large number of online calibration samples. Experimental results show that through transferring from a large amount of historical data, decoder can achieve satisfactory classification accuracy with only a small amount of online data.

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Research on walking-aid robot motion control with both compliance and safety

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Zidonghua Xuebao/Acta Automatica Sinica 2016年第12期42卷 1859-1873页

作者： Xu, Wen-Xia Huang, Jian Yan, Qing-Yang Wang, Yong-Ji Tao, Chun-Jing Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan430074 China National Rehabilitation Center Beijing100176 China

Aimed at compliance and safety problems in motion control of walking-aid robot, a multi-sensor fusion based walking-aid robot motion control method with both compliance and safety is proposed. Firstly, the mechanism, control theory and multi-sensor system of the walking-aid robot are introduced. Then according to multi-sensor system, user motion intention estimation methods for each sensor are designed and a multi-sensor based compliance motion control for walking-aid robot is proposed. After analyzing user's possible falling modes, a Kalman filter (KF) based sequential probability ratio test (SPRT) method and decision function are used to detect the fall and falling mode. Finally, several compliance motion control experiments and fall detection experiments are described to show validity of the proposed algorithm. Copyright © 2016 Acta Automatica Sinica. All rights reserved.

关键词： Robots

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Brain MRI Segmentation with Patch-based CNN Approach

Brain MRI Segmentation with Patch-based CNN Approach

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第35届中国控制会议

作者： Zhipeng CUI Jie YANG Yu QIAO Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University The Key Laboratory of Ministry of Education for System Control and Information Processing

ISBN: (纸本)9781509009107

Brain Magnetic Resonance image(MRI) plays a non-substitutive role in clinical *** symptom of many diseases corresponds to the structural variants of *** structure segmentation in brain MRI is of great importance in modern medical *** methods were developed for automatic segmenting of brain MRI but failed to achieve desired *** this paper,we proposed a new patch-based approach for automatic segmentation of brain MRI using convolutional neural network(CNN).Each brain MRI acquired from a small portion of public dataset is firstly divided into *** of these patches are then used for training CNN,which is used for automatic segmentation of brain *** results showed that our approach achieved better segmentation accuracy compared with other deep learning methods.

关键词： CNN Deep Learning Brain MRI Segmentation Patch-based

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Design and analysis of minimizing torque ripple for direct torque controlled permanent magnet synchronous motor based on fuzzy control method

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Journal of Computational and Theoretical Nanoscience 2016年第6期13卷 3706-3713页

作者： Ning, Bowen Cheng, Shanmei Qin, Yi Key Laboratory of Education Ministry for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan Hubei430074 China

This paper is presented to examine and improve the performance of the torque ripple suppression for direct torque controlled permanent magnet synchronous motor by using fuzzy control method. On the basis of analyzing the torque ripple of the direct torque control in details, the fuzzy controllers are designed for two parts. Since the errors of the torque and flux could not assess accurately in hysteresis structure, the hysteresis controllers of the conventional DTC method are replaced by the fuzzy controller which could select the optimal voltage vector by judging the deviation degree of the torque and flux errors according to the fuzzy logic inference. Then the action time of the selected optimal voltage vector is determined by using the fuzzy duty ratio control method, the ripples of torque and flux can be attenuated effectively. The comprehensive simulation experiment is exhibited to reveal the excellent response performance of the system and verify the feasibility of the proposed fuzzy control scheme. Copyright © 2016 American Scientific Publishers. All rights reserved.

关键词： Fuzzy control

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The study on Dynamics of Functional Brain network:Based on Symbolic Dynamics

The study on Dynamics of Functional Brain network:Based on S...

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第28届中国控制与决策会议

作者： Miao Wang Yapeng Li Xi Chen Wei Li College of Automation Huazhong University of Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China

ISBN: (纸本)9781467397155

It is an important means to reveal mysteries of the brain with information theory,but scientists rarely use this method for the study of the nervous system in recent *** this study,it's the first time,by using the concepts of information theory,to exploratory investigate the dynamic properties of time course data of fMRI and functional brain *** show that the symbolic dynamics method can reduce the noise of the MRI datasets,and highlight the differences between the *** elderly group,the regions with larger entropy are mainly located in prefrontal regions,basal ganglia ***,there are significant differences in the search information entropy between the young and elderly *** results have significance for the interpretation of the brain mechanisms,which may be the basis for follow-up studies of the brain mechanisms.

关键词： fMRI Symbolic dynamics Probabilistic entropy Search information entropy

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Objective resolution measurement in single particle reconstructions based on a new spectral signal-to-noise ratio estimation

Objective resolution measurement in single particle reconstr...

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第28届中国控制与决策会议

作者： Shuai Wang Biao Zhang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467397155

The resolution measurement of 3D reconstructed density map in single particle reconstruction is an important and still an open *** this paper,we propose a new protocol to measure the resolution just from the reconstructed density *** approach estimates spectral signal-to-noise ratio(SSNR) of 3D reconstructed map by computing the ratio of signal power to noise power in frequency *** power distributions of signal and noise are estimated from structure particle region and surrounding region segmented by applying a mask *** proposed protocol of calculating SSNR,which we term mask-SSNR(mSSNR),is independent of the reconstruction algorithms and can be used for density maps reconstructed with any reconstruction ***,the mSSNR neither needs to split the dataset into halves like the Fourier shell correlation(FSC) approach,nor any original images or intermediate data like other SSNR calculation methods in this *** mSSNR provides a direct calculation of SSNR based on its original definition,and is proven to be a better approach.

关键词： Electron microscopy Resolution measurement Spectral signal-to-noise ratio Signal processing

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A robust cell tracking framework by fusing global and local optimization algorithms

A robust cell tracking framework by fusing global and local ...

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第28届中国控制与决策会议

作者： Shu Meng Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467397155

Automated cell tracking is an important branch of multi-object tracking,which can be used for quantitatively analyzing cell migration,proliferation and *** this paper,we proposed a hierarchical tracking method,fusing the global optimal method in consecutive frames assignment and local optimal approach in spatial trajectory *** the process,the detection errors were recognized and cell moving trajectories were completed *** also introduced the concept of clustering to measure the correlation between established short trajectories and reduce the tracking errors caused by fast *** rare information of cells was used in the linkage,the system can work well with *** experimental results show the effectiveness of our approach with cells having different density and activity.

关键词： Multi-object tracking Bipartite graph Optimization Hierarchical tracking

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Switching mechanism for TiO_2 memristor and quantitative analysis of exponential model parameters

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Chinese Physics B 2015年第8期24卷 598-604页

作者：王小平陈敏沈轶 School of Automation Huazhong University of Science and Technology Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology

The memristor, as the fourth basic circuit element, has drawn worldwide attention since its physical implementation was released by HP Labs in 2008. However, at the nano-scale, there are many difficulties for memristor physical realization. So a better understanding and analysis of a good model will help us to study the characteristics of a memristor. In this paper, we analyze a possible mechanism for the switching behavior of a memristor with a Pt/TiO2/Pt structure, and explain the changes of electronic barrier at the interface of Pt/TiO2. Then, a quantitative analysis about each parameter in the exponential model of memristor is conducted based on the calculation results. The analysis results are validated by simulation results. The efforts made in this paper will provide researchers with theoretical guidance on choosing appropriate values for(α, β, χ, γ) in this exponential model.

关键词： memristor switching behavior electronic barrier exponential model

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Assignment-Driven Multi-consensus in Second-order Multi-agent Systems via Impulsive control with Heterogeneous delays

Assignment-Driven Multi-consensus in Second-order Multi-agen...

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IEEE International Conference on control and Automation

作者： Yao-Wen Wei Jing-Wen Yi Yan-Wu Wang Jiang-Wen Xiao School of Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education

ISBN: (纸本)9781509017393

An assignment-driven multi-consensus problem of multi-agent systems with heterogeneous delays is investigated in this paper. By introducing a concept of intelligent adjustment factor, a distributed impulsive protocol is designed, in which only the delayed sampled relative positions to neighbors and the relative position to the last sampling state are utilized. Combined with the characteristic of impulsive control, three steps of model transformation are performed, and the assignment-driven multi-consensus problem of the original continuous-time system is converted to the stability problem of a discrete-time expanded analogous error system. A necessary and sufficient criterion is derived to guarantee the assignment-driven multi-consensus of the multi-agent system. An interesting simulation is proposed to demonstrate the effectiveness of the theoretical result and reveal the effect of intelligent adjustment factors on the assignment-driven multi-consensus.

关键词： Protocols Delays Multi-agent systems Network topology Automation Stability criteria

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Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

arXiv

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arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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