Soo-Chang Pei
National Taiwan University
527 Papers
4.2K Citations
Soo-Chang Pei is an academic researcher from National Taiwan University. The author has contributed to research in topics: Fractional Fourier transform & Digital filter. The author has an hindex of 53, co-authored 519 publications. Previous affiliations of Soo-Chang Pei include National Taiwan University of Science and Technology.
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Papers
Visual enhancement via reinforcement parameter learning for low backlighted display
Chih-Tsung Shen,Ching-Hao Lai,Yi-Ping Hung,Soo-Chang Pei +3 more
- 27 Nov 2017
TL;DR: Experimental results show that the visual enhancement via reinforcement parameter learning outperforms the existing systems.
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Modifications to the Liang, McClellan and Parks computer program for designing optimal recursive multirate digital filters
Soo-Chang Pei,Sy-Been Jaw +1 more
TL;DR: A class of recursive digital filters of the form H(z)=A(z)/B(z/sup D/) for sampling rate conversion is designed with an iterative Remez exchange algorithm to reduce the multiplication rate and the coefficients memory storage.
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Design of discrete Fractional Hilbert transformer in time domain
Soo-Chang Pei,Peng-Hua Wang,Chia-Huei Lin +2 more
- 18 May 2008
TL;DR: Through the time domain analysis of the ideal input and output signal of a LTI system, it is shown that the difference between the designed system and the ideal system can be represented by the Peano kernel of the system.
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Distributed compressive sensing: Performance analysis with diverse signal ensembles
Sung-Hsien Hsieh,Wei-Jie Liang,Chun-Shien Lu,Soo-Chang Pei +3 more
- 23 Oct 2017
TL;DR: In this article, a new factor called Euclidean distances between signals is introduced for the performance analysis of a deterministic signal model under the MMV framework, and the authors show that by taking the size of signal ensembles into consideration, MMVs exhibit better performance than SMV.
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•Posted Content
Fast Binary Embedding via Circulant Downsampled Matrix - A Data-Independent Approach.
TL;DR: It is proved if data have sparsity, this scheme can achieve similarity-preserving well and it is demonstrated that though the method is cost-effective and fast, it still achieves comparable performance in image applications.
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