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http://dx.doi.org/10.17661/jkiiect.2022.15.2.117

Performance Analysis of Noisy Group Testing for Diagnosis of COVID-19 Infection  

Seong, Jin-Taek (Department of Convergence Software, Mokpo National University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.15, no.2, 2022 , pp. 117-123 More about this Journal
Abstract
Currently the number of COVID-19 cases is increasing rapidly around the world. One way to restrict the spread of COVID-19 infection is to find confirmed cases using rapid diagnosis. The previously proposed group testing problem assumed without measurement noise, but recently, false positive and false negative cases have occurred during COVID-19 testing. In this paper, we define the noisy group testing problem and analyze how much measurement noise affects the performance. In this paper, we show that the group testing system should be designed to be less susceptible to measurement noise when conducting group testing with a low positive rate of COVID-19 infection. And compared with other developed reconstruction algorithms, our proposed algorithm shows superior performance in noisy group testing.
Keywords
Noisy Group Testing; Sparse Recovery; Diagnosis of COVID-19;
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Times Cited By KSCI : 1  (Citation Analysis)
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