A novel water vapor Raman Lidar is developed at a solar-blind wavelength of 266nm. To obtain signals of Mie-Rayleigh scattering spectra and Raman scattering spectra of H2O, N2 and O2 with fine separation and high efficient extraction, a newly high-efficiency Raman polychromatic system is designed using the combination of dichroic mirrors and narrow– band interference filters. Using the standard atmospheric scattering models and aerosol extinction coefficients, the rejection rate of Mie-Rayleigh scattering signals and the signal-to-noise ratio of atmospheric water vapor measurement are simulated. The optimal parameters of Lidar system are obtained based on the detailed analysis and the discussion of the SNR of echo signals. Lidar emission wavelength and Raman scattering echo wavelengths are all in the ultraviolet range below 300nm known as the “solar-blind” region, because practically all radiation at these wavelengths is absorbed by the ozone layer in the stratosphere. It has the advantage of detecting water vapor in the daytime without the influence of solar background radiation in the system. Through the comparison between the Raman Lidars at the wavelengths of 266nm and 355nm respectively, it is concluded that the detection performance of the designed system at 266nm is better than the Raman Lidar system at 355nm during the daytime measurement, and the measurement height can be up to the 4 km.
To solve the problem of a large amount of invalid sample and accumulations yielded by random sampling when randomized hough transform (RHT) is used to detect circles in complex images processing, an new improved arithmetic for circle detection is developed in this paper. It not only uses gradient direction information to determine whether the three sampling points should be accumulated or not, but also uses the regular hexagon window to narrow the searching range of pixels to improve the operating speed. The problem of invalid sampling and accumulations and multi-circle detection is well solved. The experiment results show that this algorithm has higher speed, smaller storage and better detection performance.
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