Feature Layer Fusion of Linear Features and Empirical Mode Decomposition of Human EMG Signal

To explore the influence of the fusion of different features on recognition,this paper took the electromyography(EMG)signals of rectus femoris under different motions(walk,step,ramp,squat,and sitting)as samples,linear features(time-domain features(variance(VAR)and root mean square(RMS)),frequency-domain features(mean frequency(MF)and mean power frequency(MPF)),and nonlinear features(empirical mode decomposition(EMD))of the samples were extracted.Two feature fusion algorithms,the series splicing method and complex vector method,were designed,which were verified by a double hidden layer(BP)error back propagation neural network.Results show that with the increase of the types and complexity of feature fusions,the recognition rate of the EMG signal to actions is gradually improved.When the EMG signal is used in the series splicing method,the recognition rate of time-domain+frequency-domain+empirical mode decomposition(TD+FD+EMD)splicing is the highest,and the average recognition rate is 92.32%.And this rate is raised to 96.1%by using the complex vector method,and the variance of the BP system is also reduced.

An Effective Thermal Splicing Method to Join Fluoride and Silica Fibers for a High Power Regime

A repeatable and simple thermal splicing method for low loss splice between fluoride and silica fibers is presented. The minimum splicing loss of 0.58 dB is achieved experimentally with this approach, Meanwhile, the power capacity of this splicing joint is also tested with a high power fiber laser. The maximum input power is up to 15 W, only limited by the available power of the laser source. To the best of our knowledge, this is the first report on thermal splicing between fluoride and silica fibers operating in a high power regime without any complicated ion-assisted deposition process.