The Korea Superconducting Tokamak Advanced Research (KSTAR) project is the major effort of the national fusion programme of the Republic of Korea. Its aim is to develop a steady state capable advanced superconducting tokamak to establish a scientific and technological basis for an attractive fusion reactor. The major parameters of the tokamak are: major radius 1.8 m, minor radius 0.5 m, toroidal field 3.5 T and plasma current 2 MA, with a strongly shaped plasma cross-section and double null divertor. The initial pulse length provided by the poloidal magnet system is 20 s, but the pulse length can be increased to 300 s through non-inductive current drive. The plasma heating and current drive system consists of neutral beams, ion cyclotron waves, lower hybrid waves and electron cyclotron waves for flexible profile control in advanced tokamak operating modes. A comprehensive set of diagnostics is planned for plasma control, performance evaluation and physics understanding. The project has completed its conceptual design and moved to the engineering design and construction phase. The target date for the first plasma is 2002.

VLSI implementations of DSP computations must be efficient, and also guarantee numerical correctness. This can b e achieved through wordlength optimization which trades precision for VLSI measures such as area, speed and power. We present a general search-based methodology for wordlength optimization in VLSI/DSP synthesis. Our methodology is based on statistical precision analysis a n d incorporation of VLSI measures into a n objective function through wordlength parameterization. We use a n abstract VLSI model simplified by partitioning the system into basic components a n d express VLSI measures as functions of wordlengths. This allows us to formulate a n optimization problem for VLSI synthesis under finite precision constraints, or t o evaluate VLSI costs/performance after t h e optimization. We show how a general search-based wordlength optimization can produce optimal or near-optimal solutions for different objective-constraint formulations of various applications. 0-7803-2123-5/94 $4.00 0 1994 IEEE

A simple liquid crystal display (LCD) backlight unit (BLU) comprising only a single-sheet polydimethylsiloxane (PDMS) light-guide plate (LGP) has been developed. The PDMS LGP, having micropatterns with an inverse-trapezoidal cross section, was fabricated by backside 3-D diffuser lithography followed by PDMS-to-PDMS replication. The fabricated BLU showed an average luminance of 2878 cd/m(2) with 73.3% uniformity when mounted in a 5.08 cm backlight module with four side view 0.85cd LEDs. The developed BLU can greatly reduce the cost and thickness of LCDs, and it can be applied to flexible displays as a flexible light source due to the flexible characteristic of the PDMS itself.

Since the birth of chromosome analysis by the aid of computers, building a fully automated chromosome analysis system has been the ultimate goal. Along with many other challenges, automating chromosome classification and segmentation has been one of the major challenges especially due to overlapping and touching chromosomes. In this paper we present a novel decomposition method for overlapping and touching chromosomes in M-FISH images. To overcome the limited success of previous decomposition methods that use partial information about a chromosome cluster, we have incorporated more knowledge about the clusters into a maximum-likelihood frame work. The proposed method evaluates multiple hypotheses based on geometric information, pixel classification results, and chromosome sizes, and a hypothesis that has a maximum-likelihood is chosen as the best decomposition of a given cluster. About 90% of accuracy was obtained for two or three chromosome clusters, which consist about 95% of all clusters with two or more chromosomes.

Multicolor fluorescence in-situ hybridization (M-FISH) technique provides color karyotyping that allows simultaneous analysis of numerical and structural abnormalities of whole human chromosomes. Currently available M-FISH systems exhibit misclassifications of multiple pixel regions that are often larger than the actual chromosomal rearrangement. This paper presents a novel unsupervised classification method based on fuzzy logic classification and a prior adjusted reclassification method. Utilizing the chromosome boundaries, the initial classification results improved significantly after the prior adjusted reclassification while keeping the translocations intact. This paper also presents a new segmentation method that combines both spectral and edge information. Ten M-FISH images from a publicly available database were used to test our methods. The segmentation accuracy was more than 98% on average.