Journals >Laser & Optoelectronics Progress
ing at the slow measurement speed problem for the conventional Brillouin optical time domain analysis (BOTDA) technology, a fast BOTDA sensing system based on the coherent detecting digital optical frequency comb (DOFC) is proposed and designed. With the coherent detecting DOFC, the Brillouin gain spectrum (BGS) and the Brillouin phase spectrum (BPS) needed for sensing can be reconstructed without any frequency sweeping. Owe to the BGS and BPS, the Brillouin frequency shift distribution of the sensing fiber can be obtained without any averaging process, and thus the response time of the BOTDA sensing system can be greatly shortened. Through the experimental tests, it is can been seen that the response time of the sensing system over 10 km sensing fiber is 0.1 ms, and the detection accuracies are 1.6 ℃ for temperature and 44 με for strain, respectively. The corresponding maximum measurement deviations of this sensing system are about 0.3 ℃ for temperature and less than 10 με for strain. The experimental results show that the BOTDA sensing system based on the coherent detecting DOFC can be used to realize a fast, long distance and high accuracy sensing of temperature and strain.
.ing at the problems in the face recognition algorithm based on Weber features that the directional information is not made full use and the extracted information is also insufficient, we propose a novel face recognition method based on multi-directional Weber gradient histograms. On the basis of original differential excitation, the neighborhood pixel gradient is increased, and the improved differential excitation and Weber gradient features are extracted. The improved differential excitation and Weber direction are quantized, and the two-dimensional histograms are extracted in blocks, which are further converted into one-dimensional histogram features. The histogram features are extracted along the Weber direction. Two features are connected to form a compound feature and simultaneously the nearest neighbor classifier is used for classifying. The experiments on different face databases show that the proposed method has not only a good recognition effect, but also a relatively strong robustness to illumination, expression and partial occlusion.
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