This paper is intended primarily for the LAMOST UCAM CCD systems. The illustrations given here
show the prototype LAMOST UCAM systems. Designed as a universal CCD controller, the UCAM
system has variety options of readout modes, sampling speeds, binning options and charge clean. Its
main components, architecture and technical design are introduced here. Some important performance
characteristics about the UCAM controller and the e2v-203-82 CCD (4K by 4K, blue CCD) are tested
under laboratory conditions, such as readout noise and gain at different sampling modes and readout
speeds, CTE, dark current, QE, and fringing. Perfect CTE and less than 3 electrons / pixel system
readout noise prove that the UCAM CCD controller system meets the requirement of the LAMOST
telescope.
32 scientific CCD cameras within 16 low-dispersion spectrographs of LAMOST are used for object spectra. This paper
introduced the CCD Master system designed for camera management and control based on UCAM controller. The layers
of Master, UDP and CCD-end daemons are described in detail. The commands, statuses, user interface and spectra
viewer are discussed.
We applied a new texture segmentation algorithm to improve the segmentation of boundary areas for distinction on the
liver needle biopsy images taken from microscopes for automatic assessment of liver fibrosis severity. It was difficult to
gain satisfactory segmentation results on the boundary areas of textures with some of existing texture segmentation
algorithms in our preliminary experiments. The proposed algorithm consists of three steps. The first step is to apply the
K-View-datagram segmentation method to the image. The second step is to find a boundary set which is defined as a set
including all the pixels with more than half of its neighboring pixels being classified into clusters other than that of itself
by the K-View-datagram method. The third step is to apply a modified K-view template method with a small scanning
window to the boundary set to refine the segmentation. The algorithm was applied to the real liver needle biopsy images
provided by the hospitals in Wuhan, China. Initial experimental results show that this new segmentation algorithm gives
high segmentation accuracy and classifies the boundary areas better than the existing algorithms. It is a useful tool for
automatic assessment of liver fibrosis severity.
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