scholarly journals Nonadaptive Algorithms for Threshold Group Testing with Inhibitors and Error-Tolerance

2012 ◽  
Vol 19 (7) ◽  
pp. 903-910 ◽  
Author(s):  
Yichao He ◽  
Haiyan Tian ◽  
Xinlu Zhang ◽  
Zhiwei Wang ◽  
Suogang Gao
Keyword(s):  
Group Testing ◽  
Error Tolerance ◽  
Threshold Group Testing ◽  
Nonadaptive Algorithms

scholarly journals Nonadaptive algorithms for threshold group testing

2009 ◽  
Vol 157 (7) ◽  
pp. 1581-1585 ◽  
Author(s):  
Hong-Bin Chen ◽  
Hung-Lin Fu
Keyword(s):  
Group Testing ◽  
Threshold Group Testing ◽  
Nonadaptive Algorithms

scholarly journals Efficiently Decodable Non-Adaptive Threshold Group Testing

2019 ◽  
Vol 65 (9) ◽  
pp. 5519-5528 ◽  
Author(s):  
Thach V. Bui ◽  
Minoru Kuribayashi ◽  
Mahdi Cheraghchi ◽  
Isao Echizen
Keyword(s):  
Group Testing ◽  
Adaptive Threshold ◽  
Threshold Group Testing

Stochastic threshold group testing

2013 ◽  
Author(s):  
C. L. Chan ◽  
S. Cai ◽  
M. Bakshi ◽  
S. Jaggi ◽  
V. Saligrama
Keyword(s):  
Group Testing ◽  
Threshold Group Testing

Threshold Group Testing on Inhibitor Model

2013 ◽  
Vol 20 (6) ◽  
pp. 464-470 ◽  
Author(s):  
Huilan Chang ◽  
Hung-Lin Fu ◽  
ChiH-Huai Shih
Keyword(s):  
Group Testing ◽  
Threshold Group Testing

scholarly journals Theoretical Bounds on Performance in Threshold Group Testing Schemes

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 637
Author(s):  
Jin-Taek Seong
Keyword(s):  
Channel Coding ◽  
Coding Theory ◽  
Large Population ◽  
Group Testing ◽  
Density Ratio ◽  
Upper And Lower Bounds ◽  
Probability Of Error ◽  
Group Matrix ◽  
Minimum Number ◽  
Threshold Group Testing

A threshold group testing (TGT) scheme with lower and upper thresholds is a general model of group testing (GT) which identifies a small set of defective samples. In this paper, we consider the TGT scheme that require the minimum number of tests. We aim to find lower and upper bounds for finding a set of defective samples in a large population. The decoding for the TGT scheme is exploited by minimization of the Hamming weight in channel coding theory and the probability of error is also defined. Then, we derive a new upper bound on the probability of error and extend a lower bound from conventional one to the TGT scheme. We show that the upper and lower bounds well match with each other at the optimal density ratio of the group matrix. In addition, we conclude that when the gaps between the two thresholds in the TGT framework increase, the group matrix with a high density should be used to achieve optimal performance.

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