[Laser speckle suppression due to dynamic multiple scattering scheme introduced by oblique incidence].

Speckle suppression has been the research focus in laser display technology. In the present paper, the relation between multiple scattering and the size of speckle grains is established by analyzing the properties of speckle generated by the laser beam through SiO2 suspension. Combined with dynamic light scattering theory, laser speckle suppression due to dynamic multiple scattering scheme introduced by oblique incidence is proposed. A speckle suppression element consists of a static diffuser and a light pipe containing the water suspension of SiO2 microspheres with a diameter of 300 nm and a molar concentration of 3.0 x 10(-4) µm3, which is integrated with the laser display system. The laser beam with different incident angles into the SiO2 suspension affecting the contrast of the speckle images is analyzed by the experiments. The results demonstrate that the contrast of the speckle image can be reduced to 0.067 from 0.43 when the beam with the incident angle of approximately 8 degrees illuminates into the SiO2 suspension. The spatial average of speckle granules and the temporal average of speckle images were achieved by the proposed method, which improved the effect of speckle suppression. The proposed element for speckle suppression improved the reliability and reduced the cost of laser projection system, since no mechanical vibration is needed and it is convenient to integrate the element with the existing projection system.

[Improvement of the recognition probability about camouflage target based on BP neural network].

Using static Michelson interferometer to get the spectrum information of measurement targets for spectrum identification, under the condition that the interference length is constant, the system can be optimized by BP neural network algorithm for the mixed spectral separation process. Thereby it can realize improving the recognition probability of camouflage target. Collecting the spectrum information in field of view (FOV) by the interferometer and linear array CCD detector, composing the set of mixed spectrum data, with known absorption spectrum of the material as a hidden layer of rules, it used BP neural network to separate the mixed spectrum data. Experiment with different distances, different combinations of mixed background spectrum as the initial data, using steel target (size: 1.5 m x 1.5 m) made of four kinds, the recognition probability of non-camouflage target is about 90% by BP neural network algorithm or the traditional algorithm, while the recognition probability of camouflage target is 75.5% with BP, better than 31.7% with the traditional, so it can effectively improve the recognition probability of camouflage target.