• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.682-685
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• 2020

In this paper, we analyze the dominant noise source of conventional inductively degenerated common-source (CS) cascode low noise amplifier (LNA) when width and gate length of stacked transistors vary. Analytical MOSFET and its noise model are used to estimate the contributions of noise sources. All parameters are based on measured data of 60nm, 90nm and 130nm CMOS devices. Based on the noise analysis for different frequencies and device parameters including process nodes, the dominant noise source can be analyzed to optimize noise figure on the configuration. We verified analytically that the intuctively degenerated CS topology can not sustain its benefits in noise above a certain operation frequency of LNA over different process nodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4043-4064
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• 2021

Night-time image quality evaluation is an urgent requirement in visual inspection. The lighting environment of night-time results in low brightness, low contrast, loss of detailed information, and colour dissonance of image, which remains a daunting task of delicately evaluating the image quality at night. A new blind quality assessment metric is presented for realistic night-time scenario through a comprehensive consideration of contrast, texture, and colour in this article. To be specific, image blocks' color-gray-difference (CGD) histogram that represents contrast features is computed at first. Next, texture features that are measured by the mean subtracted contrast normalized (MSCN)-weighted local binary pattern (LBP) histogram are calculated. Then statistical features in Lαβ colour space are detected. Finally, the quality prediction model is conducted by the support vector regression (SVR) based on extracted contrast, texture, and colour features. Experiments conducted on NNID, CCRIQ, LIVE-CH, and CID2013 databases indicate that the proposed metric is superior to the compared BIQA metrics.