Z
Zixiang Xiong
Researcher at Texas A&M University
Publications - 409
Citations - 12103
Zixiang Xiong is an academic researcher from Texas A&M University. The author has contributed to research in topics: Decoding methods & Data compression. The author has an hindex of 53, co-authored 392 publications receiving 11540 citations. Previous affiliations of Zixiang Xiong include University of Hawaii at Manoa & Texas A&M University System.
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Distributed source coding for sensor networks
TL;DR: In this article, the authors presented an intensive discussion on two distributed source coding (DSC) techniques, namely Slepian-Wolf coding and Wyner-Ziv coding, and showed that separate encoding is as efficient as joint coding for lossless compression in channel coding.
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Compression of binary sources with side information at the decoder using LDPC codes
TL;DR: Low-density parity-check (LDPC) codes can be used to compress close to the Slepian-Wolf limit for correlated binary sources with side information at the decoder based on viewing the correlation as a channel and applying the syndrome concept.
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Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees (3-D SPIHT)
TL;DR: A low bit-rate embedded video coding scheme that utilizes a 3-D extension of the set partitioning in hierarchical trees (SPIHT) algorithm which has proved so successful in still image coding, which allows multiresolutional scalability in encoding and decoding in both time and space from one bit stream.
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Space-frequency quantization for wavelet image coding
TL;DR: The problem of how spatial quantization modes and standard scalar quantization can be applied in a jointly optimal fashion in an image coder is addressed and an image coding algorithm is developed for solving the resulting optimization problem.
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Optimal number of features as a function of sample size for various classification rules
TL;DR: This study employs simulation for various feature-label distributions and classification rules, and across a wide range of sample and feature-set sizes, to achieve the desired end, finding the optimal number of features as a function of sample size.