• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.1-8
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• 2010

Wireless multimedia service through the access to mobile telephone network or data network is a vital part of contemporary life, and the demand for frequency spectrum for new services is expected to explode as the ubiquitous computing proliferate. Cognitive radio is a technology, which automatically recognizes and searches for temporally and spatially unused frequency spectrum, then actively determines the communication method, bandwidth, etc. according to the environment, thus utilizing the limited spectrum resources efficiently. In this paper, we investigate the effects of imperfect sensing, misdetection and false alarm, on the primary and secondary users' spectrum usage through the analysis of continuous time Markov Chain. We analyzed the effects of the parameters such as sensing error, offered load on the system performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.9-15
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• 2010

Cognitive radio is a technology, which automatically recognizes and searches for temporally and spatially unused frequency spectrum, then actively determines the communication method, bandwidth, etc. according to the environment, thus utilizing the limited spectrum resources efficiently. In this paper, with the imperfect sensing of misdetection and false alarm, we quantitatively investigate the effects of primary and secondary user's queue on the primary and secondary users' spectrum usage through the analysis of continuous time Markov Chain. With the queue primary user's spectrum usage improved up to 18%, and the secondary user's spectrum usage improved up to 50%.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.94-99
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• 2015

With the recent machine learning paradigm of using nonparametric Bayesian statistics and statistical inference based on random sampling, the Dirichlet distribution finds many uses in a variety of graphical models. It is a multivariate generalization of the gamma distribution and is defined on a continuous (K-1)-simplex. This paper presents a sampling method for a Dirichlet distribution for the problem of dividing an integer X into a sequence of K integers which sum to X. The target samples in our problem are all positive integer vectors when multiplied by a given X. They must be sampled from the correspondingly gridded simplex. In this paper we develop a Markov Chain Monte Carlo (MCMC) proposal distribution for the neighborhood grid points on the simplex and then present the complete algorithm based on the Metropolis-Hastings algorithm. The proposed algorithm can be used for the Markov model, HMM, and Semi-Markov model for accurate state-duration modeling. It can also be used for the Gamma-Dirichlet HMM to model q the global-local duration distributions.

• Journal of the Korean Institute of Navigation
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• v.16 no.1
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• pp.94-97
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• 1992

In this paper, the individual number of the future has depended not only upon the present individual number but upon the present individual age, considering the stochastic process model of individual number when the life span of each individual number and the individual age as a set, this becomes a Markovian. Therefore, in this paper the individual is treated as invariable, without depending upon the whole record of each individual since its birth. As a result, suppose {N(t), t>0} be a counting process and also suppose $Z_n$ denote the life span between the (n-1)st and the nth event of this process, (n{$geq}1$) : that is, when the first individual is established at n=1(time, 0), the Z$Z_n$ at time nth individual breaks, down. Random walk $Z_n$ is $Z_n=X_1+X_2+{\cdots}{\cdots}+X_A, Z_0=0$ So, fixed time t, the stochastic model is made up as follows ; A) Recurrence (Regeneration)number between(0.t) $N_t=max{n ; Z_n{\leq}t}$ B) Forwardrecurrence time(Excess life) $T^-I_t=Z_{Nt+1}-t$ C) Backward recurrence time(Current life) $T^-_t=t-Z_{Nt}$