Jun Zhou

Prof. Jun Zhou

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Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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6 January 2020 in Journal of Neural Engineering
Verified
Journal Article
Closed access
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GDGuanghai Dai
Guanghai Dai
Jun ZhouJun Zhou
Jun Zhou
JHJiahui Huang
Jiahui Huang
NWNing Wang
Ning Wang
Objective . Electroencephalography (EEG) motor imagery classification has been widely used in healthcare applications such as mobile assistive robots and post-stroke rehabilitation. Recently, EEG motor imagery classification methods based on convolutional neural networks (CNNs) have been proposed and have achieved relatively high classification accuracy. However, these methods use single convolution scale in the CNN, while the best convolution scale differs from subject to subject. This limits the classification accuracy. Another issue is that the classification accuracy degrades when training data is limited. Approach . To address these issues, we have proposed a hybrid-scale CNN architecture with a data augmentation method for EEG motor imagery classification. Main results . Compared with several state-of-the-art methods, the proposed method achieves an average classification accuracy of 91.57% and 87.6% on two commonly used datasets, which outperforms several state-of-the-art EEG motor imagery classification methods. Significance . The proposed method effectively addresses the issues of existing CNN-based EEG motor imagery classification methods and improves the classification accuracy.
26 January 2021 in IEEE Transactions on Circuits and Systems I: Regular Papers
Verified
Journal Article
Closed access
SLSixu Li
Sixu Li
ZZZhaomin Zhang
Zhaomin Zhang
RMRuixin Mao
Ruixin Mao
JXJianbiao Xiao
Jianbiao Xiao
Liang ChangLiang Chang
Liang Chang
Jun ZhouJun Zhou
Jun Zhou
In the recent years, the spiking neural network (SNN) has attracted increasing attention due to its low energy consumption and online learning potential. However, the design of SNN processor has not been thoroughly investigated in the past, resulting in limited performance and energy consumption. In this work, a fast and energy-efficient SNN processor with adaptive clock/event-driven computation scheme and online learning capability has been proposed. Several techniques have been proposed to reduce the computation time and energy consumption, including Adaptive Clock- and Event-Driven Computing Scheme, Neighboring PE Borrowing Technique, Compressed Spike Routing Technique and Reconfigurable PE for Inference and Learning. Implemented on a Virtex-7 FPGA, the proposed design achieves computation time of 3.15 ms/image, inference energy consumption of $0.028~\mu $ J/synapse/image and online learning energy consumption of $0.297~\mu $ J/synapse/image for the MNIST 10-class dataset, which outperform several state-of-the-art SNN processors. The proposed SNN processor is suitable for real-time and energy-constrained applications.
9 April 2019 in IEEE Journal of Biomedical and Health Informatics
Verified
Journal Article
Closed access
JHJiahui Huang
Jiahui Huang
SLShuisheng Lin
Shuisheng Lin
NWNing Wang
Ning Wang
GDGuanghai Dai
Guanghai Dai
YXYuxiang Xie
Yuxiang Xie
Jun ZhouJun Zhou
Jun Zhou
Human activity recognition has been widely used in healthcare applications such as elderly monitoring, exercise supervision and rehabilitation monitoring. Compared with other approaches, sensor-based wearable human activity recognition is less affected by environmental noise and therefore is promising in providing higher recognition accuracy. However, one of the major issues of existing wearable human activity recognition methods is that although the average recognition accuracy is acceptable, the recognition accuracy for some activities (e.g. ascending stairs and descending stairs) is low, mainly due to relatively less training data and complex behavior pattern for these activities. Another issue is that the recognition accuracy is low when the training data from the test subject is limited, which is a common case in real practice. In addition, the use of neural network leads to large computational complexity and thus high power consumption. To address these issues, we proposed a new human activity recognition method with two-stage end-to-end CNN and a data augmentation method. Compared with the state-of-the-art methods (including neural network based methods and other methods), the proposed methods achieve significantly improved recognition accuracy and reduced computational complexity.
17 February 2020 in IEEE Transactions on Biomedical Circuits and Systems
Verified
Journal Article
Closed access
YWYing Wei
Ying Wei
Jun ZhouJun Zhou
Jun Zhou
YWYin Wang
Yin Wang
YLYinggang Liu
Yinggang Liu
QLQingsong Liu
Qingsong Liu
LHLi Huang
Li Huang
This paper reviews the state of the arts and trends of the AI-based biomedical processing algorithms and hardwares. The algorithms and hardwares for different biomedical applications such as ECG, EEG and hearing aid have been reviewed and discussed. For algorithm design, various widely used biomedical signal classification algorithms have been discussed including support vector machine (SVM), back propagation neural network (BPNN), convolutional neural networks (CNN), probabilistic neural networks (PNN), recurrent neural networks (RNN), Short-term Memory Network (LSTM), fuzzy neural network and etc. The pros and cons of the classification algorithms have been analyzed and compared in the context of application scenarios. The research trends of AI-based biomedical processing algorithms and applications are also discussed. For hardware design, various AI-based biomedical processors have been reviewed and discussed, including ECG classification processor, EEG classification processor, EMG classification processor and hearing aid processor. Various techniques on architecture and circuit level have been analyzed and compared. The research trends of the AI-based biomedical processor have also been discussed.
22 July 2019 in IEEE Transactions on Biomedical Circuits and Systems
Verified
Journal Article
Closed access
NWNing Wang
Ning Wang
Jun ZhouJun Zhou
Jun Zhou
GDGuanghai Dai
Guanghai Dai
JHJiahui Huang
Jiahui Huang
YXYuxiang Xie
Yuxiang Xie
Wearable intelligent ECG monitoring devices can perform automatic ECG diagnosis in real time and send out alert signal together with abnormal ECG signal for doctor's further analysis. This provides a means for the patient to identify their heart problem as early as possible and go to doctors for medical treatment. For such system the key requirements include high accuracy and low power consumption. However, the existing wearable intelligent ECG monitoring schemes suffer from high power consumption in both ECG diagnosis and transmission in order to achieve high accuracy. In this work, we have proposed an energy-efficient wearable intelligent ECG monitor scheme with two-stage end-to-end neural network and diagnosis-based adaptive compression. Compared to the state-of-the-art schemes, it significantly reduces the power consumption in ECG diagnosis and transmission while maintaining high accuracy.
24 February 2020 in IEEE Sensors Journal
Verified
Journal Article
Closed access
NBNan Bai
Nan Bai
YTYuan Tian
Yuan Tian
YLYe Liu
Ye Liu
ZYZhengxi Yuan
Zhengxi Yuan
ZXZhuoling Xiao
Zhuoling Xiao
Jun ZhouJun Zhou
Jun Zhou
Indoor pedestrian positioning has been widely used in applications such as fire rescue and indoor navigation. Compared with other indoor positioning technologies such as Wi-Fi and UWB, inertial measurement unit (IMU) based indoor positioning does not require external facilities and has lower cost. However, the major issue of IMU-based indoor positioning is that the inertial sensors exhibit errors and the errors accumulates, which affects the positioning precision. Existing IMU-based indoor positioning techniques have reduced the accumulated error, but still face several issues in positioning precision and system cost. In this paper, we propose a new IMU-based indoor pedestrian positioning technique. It adopts motion speed based adaptive error compensation and step detection based up/downstairs tracking to improve the positioning precision without using additional sensors. Compared with the existing techniques, the proposed technique achieves higher positioning precision (down to 0.11% for the closed error, 0.15% for the distance error and 0.52% for the height error) with less number of sensors (only accelerator and gyroscope) to lower the cost.