We consider the probabilistic group testing problem where d random defective items in a
large population of N items are identified with high probability by applying binary tests. It is …

In the problem of classical group testing one aims to identify a small subset (of size $ d $)
diseased individuals/defective items in a large population (of size $ n $) via a minimal …

The task of non-adaptive group testing is to identify up to d defective items from N items,
where a test is positive if it contains at least one defective item, and negative otherwise. If …

We consider a variant of group testing (GT) models called noisy threshold group testing
(NTGT), in which when there is more than one defective sample in a pool, its test result is …

Given up to positive items in a large population of items (), the goal of threshold group
testing is to efficiently identify the positives via tests, where a test on a subset of items is …

Given d defective items in a population of n items with d≪n, in threshold group testing
without gap, the outcome of a test on a subset of items is positive if the subset has at least u …

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 …

The main goal of group testing with inhibitors (GTI) is to efficiently identify a small number of
defective items and inhibitor items in a large set of items. A test on a subset of items is …

The group testing problem is to identify a population of K defective items in a set of n items
using the results of a small number of measurements or tests. In this paper, we study the …

The basic goal of threshold group testing is to identify up to defective items among a
population of items, where is usually much smaller than. The outcome of a test on a subset …