• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12C
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• pp.1173-1183
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• 2006

In this paper, we propose an efficient method for improving visual quality of AR-FGS (Adaptive Reference FGS) which is adopted as a key scheme for SVC (Scalable Video Coding) or H.264 scalable extension. The standard FGS (Fine Granularity Scalability) adopts AR-FGS that introduces temporal prediction into FGS layer by using a high quality reference signal which is constructed by the weighted average between the base layer reconstructed imageand enhancement reference to improve the coding efficiency in the FGS layer. However, when the enhancement stream is truncated at certain bitstream position in transmission, the rest of the data of the FGS layer will not be available at the FGS decoder. Thus the most noticeable problem of using the enhancement layer in prediction is the degraded visual quality caused by drifting because of the mismatch between the reference frame used by the FGS encoder and that by the decoder. To solve this problem, we exploit the principle of cyclical block coding that is used to encode quantized transform coefficients in a cyclical manner in the FGS layer. Encoding block coefficients in a cyclical manner places 'higher-value' bits earlier in the bitstream. The quantized transform coefficients included in the ealry coding cycle of cyclical block coding have higher probability to be correctly received and decoded than the others included in the later cycle of the cyclical block coding. Therefore, we can minimize visual quality degradation caused by bitstream truncation by adjusting weighting factor to control the contribution of the bitstream produced in each coding cycle of cyclical block coding when constructing the enhancement layer reference frame. It is shown by simulations that the improved AR-FGS scheme outperforms the standard AR-FGS by about 1 dB in maximum in the reconstructed visual quality.

• Advances in nano research
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• v.15 no.4
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• pp.295-304
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• 2023

In this paper, a powerful photocatalyst based on carbon nanocomposite is developed in order to obtain a new material applicable in water treatment and especially for the discoloration of effluents used in the textile industry. For that, TiO₂-graphene nanocomposites have been successfully synthesized by a mixture of Functionalized Graphene Sheet (FGS) and tetrachlorotitanium complexes to form FGS-TiO₂ nanocomposite. In the presence of an anionic surfactant, we used a new chemical process to functionalize graphene sheets in order to make them an excellent medium for blocking and preventing the aggregation of TiO₂ nanoparticles. The components of these nanocomposites are characterized by means of X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FT-IR), Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), which confirms the successful formation of the FGS-TiO₂ nanocomposite. It was found that the TiO₂ nanoparticles were dispersed uniformly on the graphene plane which possesses better charge separation capability than pure TiO₂. The FGS-TiO₂ nanocomposites exhibited higher photocatalytic activity compared to pure TiO₂ for the removal of three dyes: such as Methylene Blue (MB), Bromophenol Blue (BB) and Alizarin Red-S (AR) in water. The removal process was fast and more efficient with FGS-TiO₂ nanocomposite in daylight (in the absence of UV irradiation) compared to pure TiO₂ nanoparticles without and under UV in all pH range.