Browsing by Author "Liao, Chien-Chih"

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Geometrical impact on the optical polarization of droplet epitaxial quantum dots
(2012-09-17)
Liao, Yu-Huai
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Liao, Chien-Chih
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Ku, Chih-Hao
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Chang, Yu-Chen
;
Cheng, Shun-Jen
We report on experimental and theoretical investigations of the optical anisotropy of GaAs/AlGaAs quantum dots grown by droplet epitaxy. With in situ annealing in the growth, the shape of quantum dots is systematically controlled from a tall and laterally symmetric shape to a flat and laterally elongated one. Photoluminescence spectroscopy demonstrates an uncommon observation: the more elongated the quantum dots, the lower the degree of linear polarization. Theoretical analysis based on a four-band k center dot p theory reveals a substantial impact of vertical confinement on the valence heavy-hole and light-hole mixing, which leads to the enhancement of polarization anisotropy for taller quantum dots. The influence of Coulomb interactions on polarization anisotropy is studied by using the partial configuration interaction method, and is shown to reduce the polarization anisotropy through the mixing of single-particle configurations with different symmetries.
USING C3D TO DETECT REAR OVERTAKING BEHAVIOR
(2019-01-01)
Tseng, Ching-Kai
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Liao, Chien-Chih
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Shen, Po-Chun
;
Guo, Jiun-In
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電子工程學系及電子研究所
Avoiding traffic accidents is critical since the death of traffic accidents is the eighth among the top ten leading causes of death in 2018. This paper proposes a light-weight convolutional 3D (C3D) network with five 3D convolution layers and two fully-connected layers to predict overtaking behavior. This network utilizes the last layer of convolution layer to learn the overtaking object location in the final frame. Based on NVIDIA Jetson TX2, the proposed C3D network achieves 91.46% accuracy to detect overtaking behavior on rainy days. To generate this excellent deep learning model, we use an efficient labeling tool, called ezLabel, which is a free SaaS for academia group with 96,000 opened image data samples for deep learning. ezLabel owns outstanding route prediction and fitting functions, which speeds up with the factor of ten compared to traditional tools. Users only label the object in its first frame and in its final frame, and then ezLabel labels the object in all frames in between and fits the bounding box to the object. The ezLabel can be used to label objects captured with any moving or static cameras efficiently.