A near-field optical virtual probe based on the principle of near-field evanescent wave interference can be used in optical data storage, nano-lithography, near-field imaging and optical manipulation etc. The best choice of evanescent wave interference is evanescent Bessel beams that have the characteristics of both propagating Bessel beams and evanescent wave. It is concluded that evanescent Bessel beams is an evanescent wave with the characteristics of diffraction free and radial polarization. These characteristics lead to several advantages in near-field optics: the focus of radially polarized light can be quite smaller than the one of linear polarized light used commonly and diffraction free can bring in constant intensity distribution in a certain range. Meanwhile, based on the concept of conventional apodization, the idea of apodization of evanescent field is proposed to overcome some disadvantages of evanescent Bessel beams, such as the big side lobe and spread of transversal intensity. In this paper, Finite Difference Time Domain (FDTD) method is adopted to simulate the evanescent Bessel beams. Several parameters are considered as variants changeable to get the different simulation results. The better performance of the side lobe suppression and the narrow spot size are discussed. This work may be important to the application of near-field optical virtual probe in the future.
With the growth of Internet and storage capability in recent years, image has become a widespread information format in World Wide Web. However, it has become increasingly harder to search for images of interest, and effective image search engine for the WWW needs to be developed. We propose in this paper a selective filtering process and a novel approach for image classification based on feature element in the image search engine we developed for the WWW. First a selective filtering process is embedded in a general web crawler to filter out the meaningless images with GIF format. Two parameters that can be obtained easily are used in the filtering process. Our classification approach first extract feature elements from images instead of feature vectors. Compared with feature vectors, feature elements can better capture visual meanings of the image according to subjective perception of human beings. Different from traditional image classification method, our classification approach based on feature element doesn't calculate the distance between two vectors in the feature space, while trying to find associations between feature element and class attribute of the image. Experiments are presented to show the efficiency of the proposed approach.