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1. Automatic Localization of Seizure Onset Zone From High-Frequency SEEG Signals: A Preliminary Study

Author

Linxia Xiao, Caizi Li, Yanjiang Wang, Junxi Chen, Weixin Si, Chen Yao, Xifeng Li, Chuanzhi Duan, and Pheng-Ann Heng

Subjects
SEEG, preoperative planning, seizure onset zone, convolutional neural network, AHFEI, Computer applications to medicine. Medical informatics, R858-859.7, Medical technology, R855-855.5
Abstract

Objective: Stereoelectroencephalogram (SEEG) has been widely adapted to detect the electrical activity of patients with epilepsy. Due to the low-quality, large-amount, high-dimensionality characteristics of SEEG data, it is still challenging to comprehensively employ the SEEG signals to automatically and precisely determine the seizure onset zone (SOZ). This is because there is lack of an effective criterion for clinicians to select the target electrodes, which is of great importance for SOZ localization. Methods: We propose a SOZ localization method via analyzing the long-term SEEG monitoring for preoperative planning of epilepsy surgery. Considering that high frequency oscillations can reflect physiological brain activity of epileptic patients, we first extract the high-frequency features of the SEEG signals and utilize the convolutional neural network (CNN) to detect the interictal and seizure segments. Then we propose a novel criterion, namely adaptive high frequency epileptogenicity index (AHFEI), to determine the target electrodes. Results: We compare our SEEG-determined target electrodes with three preoperative planning of successful focal epilepsy resective surgery cases, finding that most localization results of our method are in consistent with clinical successful decision making, while the performance of our method outperforms than the state-of-the-art method for SOZ localization. Conclusion: Our SEEG-determined SOZ localization method can assist clinicians in preliminarily selecting the potential target electrodes according to long-term SEEG data automatically and effectively. Clinical Impact: The proposed automatic SOZ localization method has achieved satisfactory performance in the preliminary study, which has the great potential to be integrated into function-structure fused clinical decision-making system.

Published
2021
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2. Adaptive Nonlinear Control Algorithm for a Self-Balancing Robot

Author

Yun Su, Ting Wang, Kai Zhang, Chen Yao, and Zhidong Wang

Subjects
Adaptive control, center of gravity compensation, nonlinear controller, parameter adjustment, self-balancing robot, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

We previously developed a novel composite wheel-leg-track explosive ordnance disposal (EOD) robot with high mobility, able to switch between a track or a self-balancing motion mode according to environmental conditions, named Scorpio. In this paper, we propose an adaptive nonlinear control algorithm for improving the stability of the robot in the self-balancing mode. First, a model of the dynamics of the robot was established, with which we designed the nonlinear cascade controller for combined balance and motion control. With our system, the attitude of the robot is estimated using a Kalman filtering algorithm. Based on this, an adaptive adjustment algorithm amends the parameters of the controller in real time according to the state of the robot, for improved stability. In addition, we formulated an adaptive zero-offset angle identification algorithm to compensate for deviations caused by changes to the robot's center of gravity (due to changes to its mechanical structure), ensuring that this stability could be maintained. Results of experiments conducted to verify their operation show that self-balancing control of Scorpio can be achieved with the proposed algorithms.

Published
2020
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6. Trajectory-Based SLAM for Indoor Mobile Robots with Limited Sensing Capabilities

Author

Chen, Yao, primary, Rodriguez, Jeremias, additional, Karimian, Arman, additional, Pheil, Benjamin, additional, Franco, Jose, additional, Moser, Renaud, additional, Sandstrom, Read, additional, Lenser, Scott, additional, Gritsenko, Artem, additional, Tamino, Daniele, additional, Giunta, Felipe Andres Tenaglia, additional, Li, Guanlai, additional, Wasserman, Philip, additional, and Huttlin, Andrea Okerholm, additional

Published
2023
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8. Comparison of Anatomical and Diffusion MRI for detecting Parkinson’s Disease using Deep Convolutional Neural Network

Author

Chattopadhyay, Tamoghna, primary, Singh, Amit, additional, Laltoo, Emily, additional, Boyle, Christina P., additional, Owens-Walton, Conor, additional, Chen, Yao-Liang, additional, Cook, Philip, additional, McMillan, Corey, additional, Tsai, Chih-Chien, additional, Wang, J-J, additional, Wu, Yih-Ru, additional, van der Werf, Ysbrand, additional, and Thompson, Paul M., additional

Published
2023
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10. Curriculum Based Multi-Task Learning for Parkinson’s Disease Detection

Author

Dhinagar, Nikhil J., primary, Owens-Walton, Conor, additional, Laltoo, Emily, additional, Boyle, Christina P., additional, Chen, Yao-Liang, additional, Cook, Philip, additional, McMillan, Corey, additional, Tsai, Chih-Chien, additional, Wang, J-J, additional, Wu, Yih-Ru, additional, van der Werf, Ysbrand, additional, and Thompson, Paul M., additional

Published
2023
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32. Automatic Localization of Seizure Onset Zone From High-Frequency SEEG Signals: A Preliminary Study

Author

Chuanzhi Duan, Linxia Xiao, Junxi Chen, Xifeng Li, Pheng-Ann Heng, Yanjiang Wang, Weixin Si, Caizi Li, and Chen Yao

Subjects
Computer science, Brain activity and meditation, SEEG, Feature extraction, Computer applications to medicine. Medical informatics, Biomedical Engineering, R858-859.7, convolutional neural network, Seizure onset zone, Convolutional neural network, Stereoelectroencephalography, Epilepsy, medicine, Medical technology, Epilepsy surgery, Ictal, AHFEI, R855-855.5, business.industry, Pattern recognition, General Medicine, medicine.disease, preoperative planning, seizure onset zone, Artificial intelligence, business
Abstract

Objective: Stereoelectroencephalogram (SEEG) has been widely adapted to detect the electrical activity of patients with epilepsy. Due to the low-quality, large-amount, high-dimensionality characteristics of SEEG data, it is still challenging to comprehensively employ the SEEG signals to automatically and precisely determine the seizure onset zone (SOZ). This is because there is lack of an effective criterion for clinicians to select the target electrodes, which is of great importance for SOZ localization. Methods: We propose a SOZ localization method via analyzing the long-term SEEG monitoring for preoperative planning of epilepsy surgery. Considering that high frequency oscillations can reflect physiological brain activity of epileptic patients, we first extract the high-frequency features of the SEEG signals and utilize the convolutional neural network (CNN) to detect the interictal and seizure segments. Then we propose a novel criterion, namely adaptive high frequency epileptogenicity index (AHFEI), to determine the target electrodes. Results: We compare our SEEG-determined target electrodes with three preoperative planning of successful focal epilepsy resective surgery cases, finding that most localization results of our method are in consistent with clinical successful decision making, while the performance of our method outperforms than the state-of-the-art method for SOZ localization. Conclusion: Our SEEG-determined SOZ localization method can assist clinicians in preliminarily selecting the potential target electrodes according to long-term SEEG data automatically and effectively. Clinical Impact: The proposed automatic SOZ localization method has achieved satisfactory performance in the preliminary study, which has the great potential to be integrated into function-structure fused clinical decision-making system.

Published
2021

37. Adaptive Nonlinear Control Algorithm for a Self-Balancing Robot

Author

Zhidong Wang, Kai Zhang, Ting Wang, Yun Su, and Chen Yao

Subjects
nonlinear controller, General Computer Science, Computer science, General Engineering, Mode (statistics), Stability (learning theory), Adaptive control, center of gravity compensation, Motion control, Computer Science::Robotics, Nonlinear system, Cascade controller, self-balancing robot, Control theory, Robot, General Materials Science, State (computer science), lcsh:Electrical engineering. Electronics. Nuclear engineering, lcsh:TK1-9971, parameter adjustment
Abstract

We previously developed a novel composite wheel-leg-track explosive ordnance disposal (EOD) robot with high mobility, able to switch between a track or a self-balancing motion mode according to environmental conditions, named Scorpio. In this paper, we propose an adaptive nonlinear control algorithm for improving the stability of the robot in the self-balancing mode. First, a model of the dynamics of the robot was established, with which we designed the nonlinear cascade controller for combined balance and motion control. With our system, the attitude of the robot is estimated using a Kalman filtering algorithm. Based on this, an adaptive adjustment algorithm amends the parameters of the controller in real time according to the state of the robot, for improved stability. In addition, we formulated an adaptive zero-offset angle identification algorithm to compensate for deviations caused by changes to the robot's center of gravity (due to changes to its mechanical structure), ensuring that this stability could be maintained. Results of experiments conducted to verify their operation show that self-balancing control of Scorpio can be achieved with the proposed algorithms.

Published
2020

49. High Frequency Data Compression for Diesel Engine Monitoring

Author

Lianfang Wang, Liyuan Cai, Liyong Ma, Hao Luo, and Chen Yao

Subjects
Lossless compression, Diesel fuel, Computer science, Compression ratio, Bandwidth (signal processing), Run-length encoding, Data_CODINGANDINFORMATIONTHEORY, Diesel engine, Signal, Automotive engineering, Data compression
Abstract

In Marine diesel engine vibration monitoring system, a large number of high frequency data can be detected by high frequency sensors. Using data compression technology can effectively reduce the amount of data monitored by the ship vibration monitoring system and improve the communication efficiency of the system. In this paper, a lossless compression method combining variational mode decomposition (VMD) and run length encoding (RLE) for marine diesel high-frequency data is proposed. First, the signal is decomposed by VMD, and then the decomposed sub-signals and residual signals are encoded. In order to obtain better compression ratio, the compression method of RLE and Lempel-Ziv-Welch (LZW) is adopted. Experimental results show that this method can compress data effectively for high frequency signal of diesel engine morning and save network bandwidth.

Published
2021

50. Modeling and Characterization of a Novel In-Plane Dual-Axis MEMS Accelerometer Based on Self-Support Piezoresistive Beam.

Author

Jia, Chen, Yu, Mingzhi, Luo, Guoxi, Han, Xiangguang, Yang, Ping, Chen, Yao, Zhao, Libo, Wang, Lu, Wang, Yonglu, Wang, Jiuhong, and Jiang, Zhuangde

Subjects
ACCELEROMETERS, SPINDLES (Machine tools), MICROELECTROMECHANICAL systems, PIEZORESISTIVE devices
Abstract

Herein, a novel in-plane dual-axis micro-electro-mechanical system piezoresistive accelerometer was proposed based on theoretical and simulation analyses. Its self-support piezoresistive beam (SPB) possessed the features of force amplification and axial deformation to obtain high sensitivity. To clarify the influence of structural dimensions on the performance of the accelerometer and the pure axial-deformation condition of the SPB, a theoretical model was established to analyze mechanical behavior. The theoretical results were in well accordance with the simulation ones, displaying a maximum relative error of less than 10%. The theoretical and simulation results suggested that the accelerometer with thin and long support/hinge beams possesses a higher sensitivity but a lower frequency. However, the sensitivity and natural frequency increased simultaneously before the distance between SPB and support beam $D$ reached a certain value. The simulated longitudinal stress of SPB achieved the average value of 32.473 MPa, and the first-order natural frequency was nearly 13.6 kHz. Furthermore, the fabricated accelerometer was tested in a simple packaging case. The experimental results demonstrated that the proposed accelerometer had a measuring sensitivity of 0.198 mV/g/V with a maximum non-linearity of 1.00% FS and a resonant frequency of 12.93 kHz. The cross sensitivities were 11.93 and $15.97~\mu \text{V}$ /g/5V in the $y$ and $z$ axes, respectively, which were 1.21% and 1.61% of the prime-axis sensitivity. The zero offset with varying temperatures was 0.00296%/°C, and the zero drift in the long-term static stability test was 0.02 mV. All the results above revealed the promising application potential of the proposed accelerometer in vibration detection in the high-speed rotating spindle of machine tools. [2022-0057] [ABSTRACT FROM AUTHOR]

Published
2022
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