• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.278-280
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• 2019

Flow cytometer is one of the instrument that can measure various optical properties of a single cell or microparticle. These parameters including size, granularity, and fluorescence intensity are determined by the physical and optical interaction of the cells with excitation light source. However, users have some difficulties such as high cost, size of instrument, and limited fluorescence selectivity. In addition, abundant data is also unintentionally acquired even though user wants to have a single optical parameter. For these reasons, the use of flow cytometer is more challenging for researchers to apply their study. Therefore, the proposed study aims to develop a low-cost portable fluorescence acquisition system using a commercially available light-emitting diode and photodiode. It is designed by a 3D printer, and fluorescence selectivities are increased by changing of the light source / optical filter / detection sensor. Various number sets of fluorescently labeled cells were measured, and its feasibility was evaluated through the proposed system. As a result, acquried fluorescence intensities were proportional to the concentration of the cells and showed high linearity.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.1-11
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• 2019

With the development of remote sensing sensor technology, it has become possible to acquire satellite images with various spectral information. In particular, since the hyperspectral image is composed of continuous and narrow spectral wavelength, it can be effectively used in various fields such as land cover classification, target detection, and environment monitoring. Change detection techniques using remote sensing data are generally performed through differences of data with same dimensions. Therefore, it has a disadvantage that it is difficult to apply to heterogeneous sensors having different dimensions. In this study, we have developed a change detection method applicable to hyperspectral image and high spat ial resolution satellite image with different dimensions, and confirmed the applicability of the change detection method between heterogeneous images. For the application of the change detection method, the dimension of hyperspectral image was reduced by using correlation analysis and principal component analysis, and the change detection algorithm used CVA. The ROC curve and the AUC were calculated using the reference data for the evaluation of change detection performance. Experimental results show that the change detection performance is higher when using the image generated by adequate dimensionality reduction than the case using the original hyperspectral image.