The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 그룹 테스트(GT) 문제를 고려합니다. 우리는 결함이 있는 이진 신호를 성공적으로 디코딩하기 위한 오류 확률의 하한을 도출합니다. 이를 위해 우리는 정보이론에서 파노(Fano)의 불평등 정리를 활용합니다. 우리는 오류 확률이 엔트로피 함수, 풀링 행렬의 밀도 및 이진 신호의 희소성으로 제한된다는 것을 보여줍니다. 우리는 매우 희박한 신호를 디코딩하려면 풀링 행렬이 조밀해야 함을 평가합니다. 반대로, 디코딩하기 위해 조밀한 신호가 필요한 경우 작은 오류 확률을 달성하도록 희소 풀링 행렬을 설계해야 합니다.
Jin-Taek SEONG
Mokpo National University
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부
Jin-Taek SEONG, "Density of Pooling Matrices vs. Sparsity of Signals for Group Testing Problems" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 5, pp. 1081-1084, May 2019, doi: 10.1587/transinf.2018EDL8200.
Abstract: In this paper, we consider a group testing (GT) problem. We derive a lower bound on the probability of error for successful decoding of defected binary signals. To this end, we exploit Fano's inequality theorem in the information theory. We show that the probability of error is bounded as an entropy function, a density of a pooling matrix and a sparsity of a binary signal. We evaluate that for decoding of highly sparse signals, the pooling matrix is required to be dense. Conversely, if dense signals are needed to decode, the sparse pooling matrix should be designed to achieve the small probability of error.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8200/_p
부
@ARTICLE{e102-d_5_1081,
author={Jin-Taek SEONG, },
journal={IEICE TRANSACTIONS on Information},
title={Density of Pooling Matrices vs. Sparsity of Signals for Group Testing Problems},
year={2019},
volume={E102-D},
number={5},
pages={1081-1084},
abstract={In this paper, we consider a group testing (GT) problem. We derive a lower bound on the probability of error for successful decoding of defected binary signals. To this end, we exploit Fano's inequality theorem in the information theory. We show that the probability of error is bounded as an entropy function, a density of a pooling matrix and a sparsity of a binary signal. We evaluate that for decoding of highly sparse signals, the pooling matrix is required to be dense. Conversely, if dense signals are needed to decode, the sparse pooling matrix should be designed to achieve the small probability of error.},
keywords={},
doi={10.1587/transinf.2018EDL8200},
ISSN={1745-1361},
month={May},}
부
TY - JOUR
TI - Density of Pooling Matrices vs. Sparsity of Signals for Group Testing Problems
T2 - IEICE TRANSACTIONS on Information
SP - 1081
EP - 1084
AU - Jin-Taek SEONG
PY - 2019
DO - 10.1587/transinf.2018EDL8200
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2019
AB - In this paper, we consider a group testing (GT) problem. We derive a lower bound on the probability of error for successful decoding of defected binary signals. To this end, we exploit Fano's inequality theorem in the information theory. We show that the probability of error is bounded as an entropy function, a density of a pooling matrix and a sparsity of a binary signal. We evaluate that for decoding of highly sparse signals, the pooling matrix is required to be dense. Conversely, if dense signals are needed to decode, the sparse pooling matrix should be designed to achieve the small probability of error.
ER -