Thermoelastic damping in optical waveguide resonators with the bolometric effect.

Incorporating the bolometric effect, the thermoelastic damping in a nanowaveguide resonator driven by an optical gradient force is investigated in this paper. Based on the Euler-Bernoulli beam theory, the governing equation of the optowaveguide resonator is derived by considering the complex distribution of injected optical power, which has significant influence on the thermoelastic damping. By solving the heat diffusion equation, the theoretical model of the thermoelastic damping is presented. In this model, the effects of injected optical power, representative temperatures, waveguide material, and geometries on the thermoelastic damping are studied and discussed respectively. The results show that the peak value of thermoelastic damping increases as the injected optical power is increasing within a low range. Hardly any changes exist for the intrinsic energy dissipation of different materials at higher injected optical power. When the environmental temperature falls in the range of 293-500 K, the thermoelastic damping increases slowly, and then drops down quickly as a function of the dimensionless frequency. However, the thermoelastic damping monotonically decreases when the representative temperature drops to lower than 293 K. In addition, the thermoelastic damping is found to be scale dependent, particularly with the effect of injected optical power.

A de-illumination scheme for face recognition based on fast decomposition and detail feature fusion.

Almost all the face recognition algorithms are unsatisfied due to illumination variation. Feature with high frequency represents the face intrinsic structure according to the common assumption that illumination varies slowly and the face intrinsic feature varies rapidly. In this paper, we will propose an adaptive scheme based on FBEEMD and detail feature fusion. FBEEMD is a fast version of BEEMD without time-consuming surface interpolation and iteration computation. It can decompose an image into sub-images with high frequency matching detail feature and sub-images with low frequency corresponding to contour feature. However, it is difficult to determine by quantitative analysis that which sub-images with high frequency can be used for reconstructing an illumination-invariant face. Thus, two measurements are proposed to calculate weights for quantifying the detail feature. With this fusion technique, one can reconstruct a more illumination-neutral facial image to improve face recognition rate. Verification experiments using classical recognition algorithms are tested with Yale B, PIE and FERET databases. The encouraging results show that the proposed scheme is very effective when dealing with face images under variable lighting condition.