Randomized group testing for mutually obscuring defectives
Information Processing Letters
Some new bounds for cover-free families
Journal of Combinatorial Theory Series A
MFCS '94 Proceedings of the 19th International Symposium on Mathematical Foundations of Computer Science 1994
Group Testing Problems with Sequences in Experimental Molecular Biology
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
SIAM Journal on Computing
SIAM Journal on Discrete Mathematics
Optimal Two-Stage Algorithms for Group Testing Problems
SIAM Journal on Computing
Explicit Non-adaptive Combinatorial Group Testing Schemes
ICALP '08 Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I
Nonadaptive algorithms for threshold group testing
Discrete Applied Mathematics
Improved constructions for non-daptive threshold group testing
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming
Reconstruction of hidden graphs and threshold group testing
Journal of Combinatorial Optimization
General Theory of Information Transfer and Combinatorics
Nonrandom binary superimposed codes
IEEE Transactions on Information Theory
Optimal Algorithms for Two Group Testing Problems, and New Bounds on Generalized Superimposed Codes
IEEE Transactions on Information Theory
Group testing for image compression
IEEE Transactions on Image Processing
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Group testing is a frequently used tool to identify an unknown set of defective (positive) elements out of a large collection of elements by testing subsets (pools) for the presence of defectives. Various models have been studied in the literature. The most studied case concerns only two types (defective and non-defective) of elements in the given collection. This paper studies a novel and natural generalization of group testing, where more than one type of defectives are allowed with an additional assumption that certain obscuring phenomena occur among different types of defectives. This paper proposes some algorithms for this problem, trying to optimize different measures of performance: the total number of tests required, the number of stages needed to perform all tests and the decoding complexity.