• Journal of Information Science Theory and Practice
• /
• v.4 no.4
• /
• pp.64-74
• /
• 2016

In this paper we define a two parametric new generalized useful average code-word length $L_{\alpha}^{\beta}$(P;U) and its relationship with two parametric new generalized useful information measure $H_{\alpha}^{\beta}$(P;U) has been discussed. The lower and upper bound of $L_{\alpha}^{\beta}$(P;U), in terms of $H_{\alpha}^{\beta}$(P;U) are derived for a discrete noiseless channel. The measures defined in this communication are not only new but some well known measures are the particular cases of our proposed measures that already exist in the literature of useful information theory. The noiseless coding theorems for discrete channel proved in this paper are verified by considering Huffman and Shannon-Fano coding schemes on taking empirical data. Also we study the monotonic behavior of $H_{\alpha}^{\beta}$(P;U) with respect to parameters ${\alpha}$ and ${\beta}$. The important properties of $H_{{\alpha}}^{{\beta}}$(P;U) have also been studied.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.5
• /
• pp.572-578
• /
• 2018

This paper considers Group Testing as one of combinatorial problems. The group testing first began to inspect soldier's syphilis infection during World War II and have long established an academic basis. Recently, there has been much interest in related areas because of the rediscovery of the value of the group testing. The group testing is the same as finding a few defect samples out of a large number of samples, which is similar to the inverse problem of Compressed Sensing. In this paper, we introduce the definition of the group testing, and specify the classes of the group testing and the bounds on performance of the group testing. In addition, we show a lower bound for the number of tests required to find defective samples using the theoretical theorem which is mainly used for relationship between conditional entropy and the probability of error in the information theory. We see how our result can be different from other related results.