A Group-Based Binary Splitting Algorithm for UHF RFID Anti-Collision Systems

Introduction/purpose: The purpose of group testing algorithms is to provide a more rational resource usage. Therefore, it is expected to improve the efficiency of large-scale COVID-19 screening as well. Methods: Two variants of non-adaptive group testing approaches are presented: Hwang's generalized binary-splitting algorithm and the matrix strategy. Results: The positive and negative sides of both approaches are discussed. Also, the estimations of the maximum number of tests are given. The matrix strategy is presented with a particular modification which reduces the corresponding estimation of the maximum number of tests and which does not affect the complexity of the procedure. This modification can be interesting from the applicability viewpoint. Conclusion: Taking into account the current situation, it makes sense to consider these methods in order to achieve some resource cuts in testing, thus making the epidemiological measures more efficient than they are now.

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Smart Grid Malicious Meter Inspection by using an Adaptive Binary Splitting Algorithm

2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN) ◽

10.1109/vitecon.2019.8899427 ◽

2019 ◽

Author(s):

Hari Chandana Y. ◽

Praveen Kumar K. ◽

Siva Prasad P.

Keyword(s):

Smart Grid ◽

Splitting Algorithm ◽

Binary Splitting

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The brief comparison of the operational efficiency of pool-testing strategies for COVID-19 mass testing in PCR laboratories

10.1101/2020.07.14.20151415 ◽

2020 ◽

Author(s):

Kirill Vechera

Keyword(s):

Computer Program ◽

Operational Efficiency ◽

Splitting Algorithm ◽

Maximum Reduction ◽

Testing Strategies ◽

Operational Characteristics ◽

High Prevalence ◽

Binary Splitting ◽

Low Prevalence ◽

Efficiency Indicators

This paper addresses the operational efficiency of different pool-testing strategies in typical scenarios of a PCR laboratory working in mass testing for COVID-19 with different values of prevalence, limitations and conditions of testing, and priorities of optimization. The research employs a model of the laboratory's testing process, created after interviewing of PCR laboratories and studying their operations. The limitations and operational characteristics of this model were applied in a simulation of the testing process with different pool-testing strategies managed by a computer program developed in the LOMT project. The efficiency indicators assessed are the number of assays needed to obtain results of a batch of specimens, the number of specimens identified after the first analysis, and total time to obtain all results. Depending on prevalence, constraints of testing, and priorities of optimization, different pool-testing strategies provide the best operational efficiency. The binary splitting algorithm provides the maximum reduction of the number of assays: from 1.99x reduction for a high prevalence (10%) to 25x reduction for a low prevalence (0.1%), while other algorithms provide the least amount of time to obtain results or the maximum number of the specimens classified after the first analysis.

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