title: Nonadaptive algorithms for threshold group testing with inhibitors and error-tolerance.
abstract: A group test gives a positive (negative) outcome if it contains at least u (at most l) positive items, and an arbitrary outcome if the number of positive items is between thresholds l and u. This problem introduced by Damaschke is called threshold group testing. It is a generalization of classical group testing. Chen and Fu extended this problem to the error-tolerant version and first proposed efficient nonadaptive algorithms. In this article, we extend threshold group testing to the k-inhibitors model in which a test has a positive outcome if it contains at least u positives and at most k-1 inhibitors. By using (d + k - l, u; 2e + 1]-disjunct matrix we provide nonadaptive algorithms for the threshold group testing model with k-inhibitors and at most e-erroneous outcomes. The decoding complexity is O(n(u+k) log n) for fixed parameters (d, u, l, k, e).
authors.affiliation: Information Engineering School, Shijiazhuang University of Economics, Shijiazhuang, China.
authors.forename: Yichao
authors.initials: Y
authors.lastname: He
authors.forename: Haiyan
authors.initials: H
authors.lastname: Tian
authors.forename: Xinlu
authors.initials: X
authors.lastname: Zhang
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Wang
authors.forename: Suogang
authors.initials: S
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D006801:Humans; D008962:Models, Theoretical