We propose that nanomaterials can be used for fibers. A novel nano-InP doped fiber has been fabricated by the method of
modified chemical vapor deposition (MCVD).It has been measured that the doping concentration is 0.1%. The
relationship between refractive index and the wavelength is obtained by fitting experimental data to Sellmeier equation.
Dispersion of the fiber has been calculated in the wavelength range 1.2-1.6μm. As the wavelength varies from 1.2μm to
1.60μm, dispersion parameter D increases but is always negative. It has found that the dispersion of nano-InP doped
fibers is strongly changed compared to standard single-mode fibers, due to the nano-InP dopant which lead to a higher
refractive index difference.
As the key of these optical devices which are widely used in the communication system, high nonlinear optical fibre
will play an important role in the future optical fibre communication. With recent growth of nano-technology,
researchers are hoping to obtain some kinds of optical fibre by combining the optical fibre with the nanotechnology.
According to this current situation, the optical fibre doped with nano-material as InP (indium
phosphide) is manufactured by using the MCVD (modified chemical vapor deposition) technology after our
comprehensive consideration of many relative factors. Proved by experiments, this novel optical fibre has an
excellent waveguide characteristic. After a consideration of the model of this novel optical fibre, its propagation
constant β has been simulated by using the FEM (finite element method), and the graphs of presentation
of magnetic field of the core are also obtained. In accordance with the results, the effective refractive index
neff = 1.401 has be calculated. Both the calculated result and the simulated graphs are matching well with the
test, and this result is a step-stone bridge for future research of nonlinear parameter on this novel optical fiber.
We revisited texture saliency, took it to color images, introduced it to airborne remote sensing image processing and then
deduced an objective evaluation to local and global saliency. We proposed a model to implement saliency computation,
decomposed input image into three channels, cut down the computational expense to the lowest level, extracted texture
saliency from the different channels respectively, combined them in an innovative way, and then detected the saliency
hierarchy. Distinct from previous approaches in the domain, our method based on biological model and had clear
physical meaning. Our method could robustly work no matter how complex the input images were.
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