• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.84.2-84.2
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• 2011

In this research, two thin film deposition techniques, Atomic Layer Deposition and Sputtering are carried out for the fabrication of Yittria Stabilized Zirconia electrolyte for thin film Solid Oxide Fuel Cell. Zirconium to Yittrium ratio for both cases is about 1/8. Scanning Electron Microscope(SEM) image shows that the growth rate per hour for Atomic Layer Deposition is faster than for sputtering. X-ray Photo-electron Spectroscopy(XPS) shows that the peaks of both Zirconia and Yittria shift towards higher bending energy for the case of Atomic Layer deposition and thus are more strongly attached to the substrate. Later, Nyquist plot was used to compare the conductivity of Yittria Stabilized Electrolyte for both cases. The conductivity at $300^{\circ}C$ for Atomic Layer Deposited Yittria Stabilized Zirconia is found to be $5{\times}10^{-4}S/cm$ while that for sputtered Yittria Stabilized Zirconia is $2{\times}10^{-5}S/cm$ at the same temperature. The reason for better performance for Atomic Layered YSZ is believed to be the Nano-structured layer fabrication that aids in along the plane conduction as compared to the columnarly structured Sputtered YSZ.

• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.240-249
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• 2014

According to the compressive sensing theory, it is possible to perfectly reconstruct a signal only with a fewer number of measurements than the Nyquist sampling rate if the signal is a sparse signal which satisfies a few related conditions. From practical viewpoint for image applications, it is important to reduce its computational complexity and memory burden required in reconstruction. In this regard, a Block-based Compressive Sensing (BCS) scheme with Smooth Projected Landweber (BCS-SPL) has been already introduced. However, it still has the computational complexity problem in reconstruction. In this paper, we propose a method which modifies its stopping criterion, tolerance, and convergence control to make it converge faster. Experimental results show that the proposed method requires less iterations but achieves better quality of reconstructed image than the conventional BCS-SPL.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1961-1966
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• 2016

Compressive sensing has proved that a signal can be restored from less samples than the Nyquist rate. Reducing the required data rate is essential for a variety of fields including compression, transmission, and storage. It has been made lots of attempt to apply the compressive sensing theory into data intensive fields, such as image processing which needs to cover 4K and 8K pictures. In this paper, an image acquisition algorithm based on compressive sensing is proposed. It combines DCT, which can compact the energy of a image into a few coefficients, and the Noiselet transform, which is incoherent with DCT. The DCT coefficients represent the coarse structure of the images while the Noiselet information holds the fine details. Performance experiments with several images show that the proposed image acquisition algorithm not only outperforms the previous results, but also improves the reconstruction quality faster as the number of measurements increases.