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Your search keyword '"Wu, Zhaohui"' showing total 73 results
Start Over Author "Wu, Zhaohui" Publisher institute of electrical and electronics engineers (ieee)
73 results on '"Wu, Zhaohui"'

4. An Online Monitoring Scheme of Output Capacitor's Equivalent Series Resistance for Buck Converters Without Current Sensors

Author

Duan Xiaoxing, Bin Li, Dengyun Lei, Wu Zhaohui, and Jian Zou

Subjects
Electrolytic capacitor, Switched-mode power supply, Equivalent series resistance, Buck converter, Computer science, Ripple, Inductor, law.invention, Capacitor, Control and Systems Engineering, law, Electronic engineering, Current sensor, Electrical and Electronic Engineering
Abstract

Aluminum electrolytic capacitors are widely used in switching mode power supply as smoothing capacitor, but they are the weakest among power components in dc–dc converter. It is very important to monitor its electrical parameters, mainly the equivalent series resistance (ESR). A monitoring method of capacitor's ESR for buck converters is proposed in this article. Based on the calculation model of ESR, the target parameter is monitored by sampling output ripple voltage. The proposed method needs no current sensor. Whether the circuit is in continuous conduction mode or discontinuous conduction mode, it is convenient to monitor the capacitor. Implementation of the monitoring system is presented. The experimental results validate the effectiveness of the method.

Published
2021
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9. Oxide TFT Frontend Amplifiers for Flexible Sensing Systems

Author

Xu, Yuming, primary, Wu, Zhaohui, additional, Li, Bin, additional, Deng, Sunbin, additional, Zhong, Wei, additional, Li, Guijun, additional, Luo, Dongxiang, additional, Sze Yan Yeung, Fion, additional, Kwok, Hoi Sing, additional, and Chen, Rongsheng, additional

Published
2021
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12. A Novel Envelope Detector Based on Unipolar Metal-Oxide TFTs

Author

Xu, Yuming, primary, Li, Bin, additional, Deng, Sunbin, additional, Qin, Yuning, additional, Fan, Houbo, additional, Zhong, Wei, additional, Liu, Yuan, additional, Wu, Zhaohui, additional, Yeung, Fion Sze Yan, additional, Wong, Man, additional, Kwok, Hoi Sing, additional, and Chen, Rongsheng, additional

Published
2020
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49. Decoding Motor Cortical Activities of Monkey: A Dataset

Author

Zhou, Luoqing, Qi, Yu, Wang, Yueming, Pan, Gang, Wang, Yiwen, Zheng, Xiaoxiang, Wu, Zhaohui, Zhou, Luoqing, Qi, Yu, Wang, Yueming, Pan, Gang, Wang, Yiwen, Zheng, Xiaoxiang, and Wu, Zhaohui

Abstract

Motor brain-machine interface (BMI) has great potentials in neural motor prostheses and has received increasing attention during the past decades in the neural engineering field. It requires an approach to decode neural activities that represents desired movements. Much of the progress in decoding algorithms has been driven by the availability of neural data, e.g. spike trains, in some research groups having animal laboratories and capable of performing surgery and building BMI systems. However, researchers in the neural signal processing field often face a dilemma of lacking neural data. To continue the innovation in decoding algorithms, this paper introduces a public neural dataset, the ZJU Neural Decoding Dataset (ZJUNDD). We give the detailed paradigm of the BMI system on monkey, including the experimental setup and the collection of 96-channel motor cortical activities. The dataset contains spike rates of neurons obtained by a consistent spike sorting method. To improve the data quality and reduce outliers, the spike data are carefully selected according to the quality of hand movements of the monkey. A standard protocol is provided for the assessment of decoding algorithms on the dataset, including the partition of training and testing sets, and the evaluation metrics. We also build an online evaluation system in order to enable a fair comparison between decoding approaches. Further, we benchmark several existing algorithms, which provides a basic performance of the methods. To the best of our knowledge, this is the first public dataset of spike trains for the decoding research of motor cortical activities. © 2014 IEEE.

Published
2014

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