Phenylketonuria is a genetic disease, which causes the metabolization disorder of phenylalanine, this disorder would
damage the neural system of infants as a result of the accumulation of phenylalanine in blood. Therefore, it is of great
importance to diagnose and treat phenylketonuria as early as possible for newborns. The aim of this paper is to develop a
fluorescence detection system to measure blood phenylalanine concentration of new-born infants. In this design, a high
luminance ultraviolet LED is used for excitation source, and a kind of bifurcated optical fiber assembly is applied for
conduction of light. The excitation source is filtered and coupled into quartz fibers of the bifurcated fiber assembly for
conduction of light to excite the fluorescence of phenylalanine in blood sample. The collected fluorescence is transmitted
along the glass fibers of the assemblies and coupled to a photomultiplier tube. The fluorescence is filtered with 470~500
nm band-pass filter to subdue scattered excitation light and to limit the spectral width of the detected fluorescence. By
the comparison with a standard instrument, the new system with low power consumption, low cost and small size is also
proven sensitive and accurate, which meets the demand of clinical phenylketonuria screening.
KEYWORDS: Remote sensing, Image processing, Visual process modeling, Visualization, Brain mapping, Roentgenium, Data modeling, Data processing, RGB color model, Lithium
In this paper we use human visual attention mechanism to extract salient objects from remote sensing image. Different
from previous methods, this method just uses bottom-up features of the input image to compute saliency map. From the
saliency map we get the information about the salient object area. Guided by the saliency map, we can extract salient
object from remote image. We apply this method to remote sensing image, and the experiment shows this method gets
satisfying result.
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