Browsing by Author "Wei, SC"

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Epitaxial growth of the GaN film by remote-plasma metalorganic chemical vapor deposition
(1998-10-01)
Lai, WC
;
Chang, CY
;
Yokoyama, M
;
Guo, JD
;
Tsang, JS
GaN films were successfully grown by the remote-plasma metalorganic chemical vapor deposition (RPMOCVD) system. The composition of the GaN films could be tuned from nitrogen-rich to stoichiometric growth by varying the mole flow rate of trimethylgallium (TMGa). A hypothesis concerning the collisions between excited nitrogen and TMGa was also brought up. The collision between excited nitrogen and TMGa influences the characteristics of surface morphology, composition, growth rate, and growth mechanism. The characteristics of the GaN film were optimized by changing the growth conditions. The narrowest FWHM of the double-crystal X-ray rocking curve is about 0.2 degrees. Under optimized conditions, the composition of the GaN film is almost the same as that of the reference GaN film grown by MOCVD.
Rotation and gray-scale transform-invariant texture classification using spiral resampling, subband decomposition, and hidden Markov model
(1996-10-01)
Wu, WR
;
Wei, SC
;
電信工程研究所
;
Institute of Communications Engineering
This paper proposes a new texture classification algorithm that is invariant to rotation and gray-scale transformation, First, we convert two-dimensional (2-D) texture images to one-dimensional (1-D) signals by spiral resampling. Then, we use a quadrature mirror filter (QMF) bank to decompose sampled signals into subbands, In each band, we take high-order autocorrelation functions as features, Features in different bands, which form a vector sequence, are then modeled as a hidden Markov model (HMM), During classification, the unknown texture is matched against all the models and the best match is taken as the classification result, Simulations showed that the highest correct classification rate for 16 kinds of texture was 95.14%.
Rotation and gray-scale transform-invariant texture classification using spiral resampling, subband decomposition, and hidden Markov model
(1996-10-01)
Wu, WR
;
Wei, SC
;
電信工程研究所
;
Institute of Communications Engineering
This paper proposes a new texture classification algorithm that is invariant to rotation and gray-scale transformation, First, we convert two-dimensional (2-D) texture images to one-dimensional (1-D) signals by spiral resampling. Then, we use a quadrature mirror filter (QMF) bank to decompose sampled signals into subbands, In each band, we take high-order autocorrelation functions as features, Features in different bands, which form a vector sequence, are then modeled as a hidden Markov model (HMM), During classification, the unknown texture is matched against all the models and the best match is taken as the classification result, Simulations showed that the highest correct classification rate for 16 kinds of texture was 95.14%.