http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Junjian Zhao (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
DENSITY SMOOTHNESS PARAMETER ESTIMATION WITH SOME ADDITIVE NOISES
Zhao, Junjian,Zhuang, Zhitao Korean Mathematical Society 2018 대한수학회논문집 Vol.33 No.4
In practice, the density function of a random variable X is always unknown. Even its smoothness parameter is unknown to us. In this paper, we will consider a density smoothness parameter estimation problem via wavelet theory. The smoothness parameter is defined in the sense of equivalent Besov norms. It is well-known that it is almost impossible to estimate this kind of parameter in general case. But it becomes possible when we add some conditions (to our proof, we can not remove them) to the density function. Besides, the density function contains impurities. It is covered by some additive noises, which is the key point we want to show in this paper.
-
Random sampling and reconstruction of signals with finite rate of innovation
Yingchun Jiang,Junjian Zhao 대한수학회 2022 대한수학회보 Vol.59 No.2
In this paper, we mainly study the random sampling and reconstruction of signals living in the subspace $V^p(\Phi,\Lambda)$ of $L^p(\mathbb{R}^d)$, which is generated by a family of molecules $\Phi$ located on a relatively separated subset $\Lambda\subset \mathbb{R}^d$. The space $V^p(\Phi,\Lambda)$ is used to model signals with finite rate of innovation, such as stream of pulses in GPS applications, cellular radio and ultra wide-band communication. The sampling set is independently and randomly drawn from a general probability distribution over $\mathbb{R}^d$. Under some proper conditions for the generators $\Phi=\{\phi_\lambda:\lambda\in \Lambda\}$ and the probability density function $\rho$, we first approximate $V^{p}(\Phi,\Lambda)$ by a finite dimensional subspace $V^{p}_N(\Phi,\Lambda)$ on any bounded domains. Then, we prove that the random sampling stability holds with high probability for all signals in $V^{p}(\Phi,\Lambda)$ whose energy concentrate on a cube when the sampling size is large enough. Finally, a reconstruction algorithm based on random samples is given for signals in $V^{p}_N(\Phi,\Lambda)$.
ScienceON연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
