Browsing by Author "Lin, Pei-Ying"

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Image enhancement approach using the just-noticeable-difference model of the human visual system
(2012-07-01)
Lee, Chang-Hsing
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Lin, Pei-Ying
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Chen, Ling-Hwei
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Wang, Wei-Kang
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多媒體工程研究所
The low-contrast images taken by digital cameras or camera phones are not always satisfactory due to the limitation of the capturing devices or improper illumination/exposure conditions. Conventional image contrast enhancement methods may either fail to produce satisfactory and undistorted images, or they cannot improve every region of interest appropriately, especially faces. In this paper, a histogram equalization (HE) approach exploiting the just-noticeable-difference (JND) model of the human visual system (HVS), denoted by JND-HE, is proposed for generic image contrast enhancement. Further, the proposed JND-HE approach is combined with the exposure correction (EC) method (denoted by JND-HE-EC) for face image enhancement. The proposed JND-HE-EC approach can improve the contrast in face regions and provide proper illumination in the background. Experimental results on both generic images and faces have shown that our proposed approach can produce more pleasing and appealing enhanced images than other methods. (C) 2012 SPIE and IS&T. [DOI: 10.1117/1.JEI.21.3.033007]
Machine Learning-Driven Optimal Proactive Edge Caching in Wireless Small Cell Networks
(2019-01-01)
Lin, Pei-Ying
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Chiu, Hsiao-Ting
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Gau, Rung-Hung
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電信工程研究所
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Institute of Communications Engineering
In this paper, we propose a novel approach for proactive edge caching in wireless small cell networks. Specifically, we propose using a recurrent neural network for predicting the content popularity with low computational complexity. The mean estimation error of the adopted recurrent neural network could be very close to that of the optimal linear prediction filter utilizing all past history. Based on the predicted content popularity, we formulate and solve a minimum cost flow problem in order to optimally place content files at edge caches. Since the computational complexity of the adopted recurrent neural network is relatively low and the minimum cost flow problem can be solved in polynomial time, the proposed approach is feasible in practice. Simulation results show that the proposed approach outperforms a greedy approach and can significantly reduce the bandwidth consumption of the backhaul network.
p-type Mesoscopic Nickel Oxide/Organometallic Perovskite Heterojunction Solar Cells
(2014-04-23)
Wang, Kuo-Chin
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Jeng, Jun-Yuan
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Shen, Po-Shen
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Chang, Yu-Cheng
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Diau, Eric Wei-Guang
In this article, we present a new paradigm for organometallic hybrid perovskite solar cell using NiO inorganic metal oxide nanocrystalline as p-type electrode material and realized the first mesoscopic NiO/perovskite/[6,6]-phenyl C61-butyric acidmethyl ester (PC61BM) heterojunction photovoltaic device. The photo-induced transient absorption spectroscopy results verified that the architecture is an effective p-type sensitized junction, which is the first inorganic p-type, metal oxide contact material for perovskite-based solar cell. Power conversion efficiency of 9.51% was achieved under AM 1.5 G illumination, which significantly surpassed the reported conventional p-type dye-sensitized solar cells. The replacement of the organic hole transport materials by a p-type metal oxide has the advantages to provide robust device architecture for further development of all-inorganic perovskite-based thin-film solar cells and tandem photovoltaics.