Browsing by Author "Kuo, CC"

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A new power collection architecture for future spacecraft
(1997-10-01)
Kuo, MY
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Kuo, MS
;
Kuo, CC
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
The generalized tree-like transmission line network system having n layers in total with m-to-1 branch connection at each joint (T-TLNS-n-m) is proposed to replace the traditional series-parallel combined dc networks to automatically accumulate identically distributed power cells into the large amount of useful power for the spacecrafts, The proposed system has the attractive features of simple structure, easy construction and maintenance, and low cost, because two kinds of parts, ac current sources and m-to-1 transmission line (TL) branch connection parts, are used in the whole system. A method of implementing ac current cells having high output impedance is proposed to improve the power collection efficiency of the whole network system. Simulated results show that that low-pass nominal pi- or T-circuit with high characteristic impedance, which is used for the voltage source to current source converter in the current cell, improves the efficiency of the voltage inverter, filters out the harmonics, and increases the output impedance of the whole current cell.
Modeling and analysis of wideband power transmission line transformers
(1996)
Kuo, CC
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Kuo, MY
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Kuo, MS
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電控工程研究所
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Institute of Electrical and Control Engineering
New transmission line systems for accumulating power from distributed renewable energy
(1997-01-01)
Kuo, MY
;
Kuo, CC
;
Kuo, MS
;
交大名義發表
;
電子工程學系及電子研究所
New transmission line systems for accumulating power from distributed renewable energy
(1997-01-01)
Kuo, MY
;
Kuo, CC
;
Kuo, MS
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Quantization noise reduction for DCT coded images
(2004-08-01)
Kuo, CC
;
Chang, WT
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電信工程研究所
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Institute of Communications Engineering
By modelling the quantization error as additive white noise in the transform domain, Wiener filter is used to reduce quantization noise for DCT coded images in DCT domain. Instead of deriving the spectrum of the transform coefficient, a DPCM loop is used to whiten the quantized DCT coefficients. The DPCM loop predicts the mean for each coefficient. By subtracting the mean, the quantized DCT coefficient is converted into the sum of prediction error and quantization noise. After the DPCM loop, the prediction error can be assumed uncorrelated to make the design of the subsequent Wiener filter easy. The Wiener filter is applied to remove the quantization noise to restore the prediction error. The original coefficient is reconstructed by adding the DPCM predicted mean with the restored prediction error. To increase the prediction accuracy, the decimated DCT coefficients in each subband are interpolated from the overlapped blocks.
Speeding up Chinese character recognition in an automatic document reading system
(1998-11-01)
Tseng, YH
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Kuo, CC
;
Lee, HJ
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資訊工程學系
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Department of Computer Science
In this paper, we present two techniques for speeding up character recognition. Our character recognition system, including the candidate-cluster selection and modified branch-and-bound detail-matching modules, is implemented using two statistical features: crossing-counts and contour-direction counts. In the training stage, we divide characters into different clusters by using reference characters. To have a very high recognition rate, the candidate-cluster selection module selects the top 60 clusters with minimal distances from among 300 predefined clusters. To further speed-up the recognition speed, we use a modified branch-and-bound algorithm in the detail-matching module. In the automatic document reading system, characters and punctuation marks are first extracted from printed document images and sorted according to their positions and the document orientation. The system then recognizes all printed Chinese characters between pairs of punctuation marks. The results are then spoken aloud by a speech-synthesis system. The character recognition system and the text-to-speech synthesis system are integrated in the Windows-based document reading system, which provides a user-friendly environment. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Speeding-up Chinese character recognition in an automatic document reading system
(1997)
Tseng, YH
;
Kuo, CC
;
Lee, HJ
;
交大名義發表
;
資訊工程學系
In this paper, we present two techniques for speeding up character recognition. Our character recognition system, including the candidate-cluster selection and detail-matching modules, is implemented using two statistical features: crossing-counts and contour-direction counts. In the training stage, we divide characters into different clusters. To keep very high recognition rate, the candidate-cluster selection module selects the top 60 clusters with minimal distances from among 300 predefined clusters. To further speed-up the recognition speed, we use a modified branch-and-bound algorithm in the detail-matching module. In the automatic document-reading system, characters and punctuation marks are first extracted from printed document images and sorted according to their positions and the document orientation. The system then recognizes all printed Chinese characters between pairs of punctuation marks. The results are then spoken aloud by a speech-synthesis system.
Typeface identification for printed Chinese characters
(1998-03-01)
Tseng, YH
;
Kuo, CC
;
Lee, HJ
;
資訊工程學系
;
Department of Computer Science
In this paper, we propose a methodology for identifying typefaces of printed Chinese characters in documents. Three kinds of features, stroke width means, stroke width variations, and aspect ratio, are first used to classify character typefaces as: Black, Li, Kai-Round, or Ming-Song. Each of the last two groups contains two typefaces. Vertical/horizontal stroke width ratios are used to distinguish between the Ming and Song typefaces and accumulative pixel ratio to distinguish between the Kai and Round typefaces. Six different typeface feature distributions measured from 5401 printed Chinese characters are considered, and a trapezoid-shaped membership function is constructed for each distribution. Based on these membership functions, we determine what typeface each input character belongs to using a two-level decision tree. To increase the identification rate, the typeface of a certain character is adjusted according to the typeface identification results of the front and the next characters. In the character recognition system, we use two statistical features: crossing counts and contour directional counts. We achieved an 89.87% typeface identification rate in our experiments, and a 95.60% character recognition rate.