Biometric systems allow recognition and verification of an individual through his or her physiological or behavioral characteristics. It is a growing field of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on compression sensing was studied using bioelectric signals, which aimed to increase the limited resources of portable remote bioelectric signal recognition equipment. Using electrocardiograms (ECGs) and photoplethysmograms (PPGs) of heart signals as research data, an improved segmented weak orthogonal matching pursuit (OMP) algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis. The accuracy of the proposed method and the practicability of compression sensing in cardiac signal identification were verified. Experiments showed that the reconstructed ECG and PPG signal recognition rates were 95.65% and 91.31%, respectively, and that the residual value was less than 0.05 mV, which indicates that the proposed method can be effectively used for two bioelectric signal compression reconstructions.

In order to study the contribution of each harmonic to the output torque and axial torque of the axial magnetic gear with Halbach permanent magnet arrays (HAMG), torque and axial force calculation formulas of the HAMG are proposed based on the air-gap flux density distribution of the HAMG. Because of the difference of the air-gap flux densities at different radii, two simplified torque and axial force calculation formulas are proposed and compared. To improve the torque capability of the HAMG, parametric analysis of eight dimensional parameters is firstly conducted. By parametric analysis, six parameters such as the inner radius have been found to have obvious impact on the output torque and output torque density of the HAMG. The optimization using Maxwell software is then executed for maximizing the output torque density of the HAMG. The output torque density of the optimized HAMG is improved from 78.1 kNm/m3 to 93.3 kNm/m3 with an increase of 19%. Furthermore, spectrum analysis is also presented to illustrate the significant output torque improvement based on the torque calculation formulas.

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The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program held its first mobile facility (AMF) field campaigns in Shouxian in eastern China. Based on the AMF data, we studied the radiative properties of aerosols in late autumn and early winter. The results show that aerosols and clouds decreased the surface total radiation flux (RF) by 27.5% and the shortwave (SW) RF by 30.8%. The aerosol radiative effect (ARE) in late autumn and early winter calculated is about -24.9 W/m2. In addition, we compared the AMF data with MODIS datasets in a 1×1 degree box. The net SW errors of Terra and Aqua were 97.4 and 30.0 w/m2. The net LW errors were 17.4 W/m2and 21.4 W/m2, respectively. The differences of the errors between Terra and Aqua were caused by the different zenith angles and the different atmospheric aerosol and vapor backgrounds during the satellite overpasses.

In the speech recognition technology, feature extraction is essential for the system recognition rate, taking amount of strategies to find the better feature vectors are most researchers target. This paper presents a method of extracting feature of audio signal based on the discrete wavelet transform, then decomposed the coefficient matrix by the matrix analysis way, through this method to find a new thinking on the way of extracting feature vector. The method can be achieved in the procedure. The main purpose is to reduce the dimension of feature vector, make the vector briefer, and then reduce the computing complexity in the embedded system. This method can reduce the feature vectors dimension, accelerated the computing velocity.