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- Jen-Chun Chang, Rong-Jaye Chen, Torleiv Kløve, Shi-Chun Tsai
- IEEE Trans. Information Theory
- 2003

Mappings of the set of binary vectors of a fixed length to the set of permutations of the same length are useful for the construction of permutation codes. In this correspondence, several explicit constructions of such mappings preserving or increasing the Hamming distance are given. Some applications are given to illustrate the usefulness of the… (More)

- Jen-Chun Chang
- IEEE Transactions on Information Theory
- 2005

Mappings from the set of binary vectors of a fixed length to the set of permutations of the same length that strictly increase Hamming distances except when that is obviously not possible are useful for the construction of permutation codes. In this correspondence, we propose recursive and explicit constructions of such mappings. Some comparisons show that… (More)

- Jen-Chun Chang
- IEEE Transactions on Information Theory
- 2006

In this correspondence, for any k ges 2, we first propose two constructions of (n,k) distance-increasing mappings (DIMs) from the set of binary vectors of length n to the set of permutations of the same length that strictly increase the Hamming distance by at least k except when it is obviously not possible. Next, we prove that for any k ges 2, there is a… (More)

- Jen-Chun Chang, Hsin-Lung Wu
- 2009 Fifth International Conference on…
- 2009

We study the problem how to construct an RFID mutual authentication protocol between tags and readers. Juels (in Journal on Selected Areas in Communications, 2006) proposed an open question which asks whether there exists a fully privacy-preserving RFID authentication protocol in which the time complexity of a successful authentication phase can be done in… (More)

- Jyh-Shyan Lin, Jen-Chun Chang, Rong-Jaye Chen, Torleiv Kløve
- IEEE Transactions on Information Theory
- 2008

Permutation arrays have found applications in powerline communication. One construction method for permutation arrays is to map good codes to permutations using a distance-preserving mappings (DPM). DPMs are mappings from the set of all q-ary vectors of a fixed length to the set of permutations of some fixed length (the same or longer) such that every two… (More)

- Jen-Chun Chang, Wanjiun Liao
- ICME
- 2001

In this paper, we propose a new approach to establishing application layer conference trees for multimedia multipoint conferences on the Internet using the Megaco/H.248 protocol, a Voice over IP (VoIP) media gateway control protocol. In existing VoIP protocols (and also legacy telephone networks), a multipoint conference takes place through an MCU, and… (More)

- Jyh-Shyan Lin, Jen-Chun Chang, Rong-Jaye Chen
- Inf. Process. Lett.
- 2006

Distance-increasing mappings (DIMs) are mappings from the set of binary vectors of a fixed length to the set of permutations of the same length that increase Hamming distances except when that is obviously not possible. In this paper, we propose new non-recursive constructions of DIMs which are based on simple compositions of permutations. In comparison… (More)

- Wei-Chin Ku, Te-Chih Chou, Hsin-Lung Wu, Jen-Chun Chang
- 2010 Fourth International Conference on Genetic…
- 2010

In this paper, we develop a watermarking scheme for image authentication and verification. The watermarking scheme for tamper detection of an image is fragile and block-based. Such a scheme has good tamper localization and high tamper detection rate. The security of our scheme depends on a secret key, even though the attacker got the original watermark… (More)

- Jen-Chun Chang, Hsin-Lung Wu
- 2009 IEEE International Symposium on Information…
- 2009

A distance-preserving mapping is a one-to-one function f from p-ary vectors of length m to q-ary vectors of length n such that any two distinct p-ary vectors are mapped to two different q-ary vectors with an equal or greater Hamming distance. A special distance-preserving mapping called a distance-increasing mapping is a mapping which increases the distance… (More)

- Jen-Chun Chang, Yi-Zhi Lu, Hsin-Lung Wu
- Signal Processing
- 2017