Two new feature extraction methods, window sample entropy and window kurtosis, were proposed, which mainly aims to complete the surface Elcctromyography （sEMG）-muscle force pattern recognition for intelligent bionic...Two new feature extraction methods, window sample entropy and window kurtosis, were proposed, which mainly aims to complete the surface Elcctromyography （sEMG）-muscle force pattern recognition for intelligent bionic limb. The inspiration is drawn from physiological process of muscle force generation. Five hand movement tasks were implemented for sEMG-muscle force data record. With two classical features： Integrated Electromyography （IEMG） and Root Mean Square （RMS）, two new features were fed into the wavelet neural network to predict the muscle force. To solve the issues that amputates＇ residual limb couldn＇t provide full train data for pattern recognition, it is proposed that force was predicted by neural network which is trained by contralateral data in this paper. The feasibility of the proposed features extraction methods was demonstrated by both ipsi- lateral and contralateral experimental results. The ipsilateral experimental results give very promising pattern classification accuracy with normalized mean square 0.58 ± 0.05. In addition, unilateral transradial amputees will benefit from the proposed method in the contralateral experiment, which probably helps them to train the intelligent bionic limb by their own sEMG.展开更多
The demultiplexing experiment from a 40 Gb/s optical time-division multiplexing signal is completed by using electro- absorption sampling window based on electronic phase-locked loop circuit for clock recovery. Error-...The demultiplexing experiment from a 40 Gb/s optical time-division multiplexing signal is completed by using electro- absorption sampling window based on electronic phase-locked loop circuit for clock recovery. Error-free demultiplexing is achieved when the launched optical power into electro-absorption sampling window reaches 5.5 dBm without optical filter following the EDFA.展开更多
文摘Two new feature extraction methods, window sample entropy and window kurtosis, were proposed, which mainly aims to complete the surface Elcctromyography （sEMG）-muscle force pattern recognition for intelligent bionic limb. The inspiration is drawn from physiological process of muscle force generation. Five hand movement tasks were implemented for sEMG-muscle force data record. With two classical features： Integrated Electromyography （IEMG） and Root Mean Square （RMS）, two new features were fed into the wavelet neural network to predict the muscle force. To solve the issues that amputates＇ residual limb couldn＇t provide full train data for pattern recognition, it is proposed that force was predicted by neural network which is trained by contralateral data in this paper. The feasibility of the proposed features extraction methods was demonstrated by both ipsi- lateral and contralateral experimental results. The ipsilateral experimental results give very promising pattern classification accuracy with normalized mean square 0.58 ± 0.05. In addition, unilateral transradial amputees will benefit from the proposed method in the contralateral experiment, which probably helps them to train the intelligent bionic limb by their own sEMG.
基金supported by National 863 High TechnologyProjects of China (No. 2007AA01Z258, 2008AA01Z15)National Natural Science Foundation of China (No.60577034, 60747002,60877042)Science Foundation of Beijing(No.4062027).
文摘The demultiplexing experiment from a 40 Gb/s optical time-division multiplexing signal is completed by using electro- absorption sampling window based on electronic phase-locked loop circuit for clock recovery. Error-free demultiplexing is achieved when the launched optical power into electro-absorption sampling window reaches 5.5 dBm without optical filter following the EDFA.
