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Nonadaptive Group Testing With Random Set of Defectives

机译:带有一组缺陷的非自适应组测试

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In a group testing scheme, a series of tests are designed to identify a small number t of defective items that are present among a large number N of items. Each test takes a group of items as input and produces a binary output indicating whether any defective item is present in the group. In a non-adaptive scheme, the tests have to be designed in one-shot. In this setting, designing a testing scheme is equivalent to the construction of a disjunct matrix, an M×N binary matrix where the union of supports of any t columns does not contain the support of any other column. In principle, one wants to have such a matrix with a minimum possible number M of rows. In this paper, we consider the scenario where defective items are random and follow simple probability distributions. In particular, we consider the cases where: 1) each item can be defective independently with probability tN and 2) each t -set of items can be defective with uniform probability. In both the cases, our aim is to design a testing matrix that successfully identifies the set of defectives with high probability. Both of these models have been studied in the literature before, and it is known that Θ(tlogN) tests are necessary as well as sufficient (via random coding) in both the cases. Our main focus is explicit deterministic construction of the test matrices amenable to above scenarios. One of the most popular ways of constructing test matrices relies on constant-weight error-correcting codes and their minimum distance. In particular, it is known that codes result in test matrices with O(t2logN) rows that identify any t defectives. We go beyond the minimum distance analysis and connect the average distance of a constant weight code to the parameters of the resulting test matrix. Indeed, we show how distance, a pairwise property of the columns of the matrix, translates to a (t+1) -wise property of the columns. With our relaxed requirements, we show that using explicit constant-weight codes (e.g., based on algebraic geometry codes) we may achieve a number of tests equal to O(t(log2N/logt)) for both the first and second cases. While only away by a factor of (logN/logt) from the optimal number of tests, this is the best set of parameters that one can obtain from a deterministic construction, and our main contribution lies in relating the group testing properties to the average and minimum distances of constant-weight codes.
机译:在组测试方案中,设计了一系列测试来识别大量N个项目中存在的少量t缺陷项目。每个测试将一组项目作为输入,并产生一个二进制输出,指示该组中是否存在任何有缺陷的项目。在非自适应方案中,必须一次性设计测试。在这种情况下,设计测试方案等效于构造分离矩阵,即M×N二元矩阵,其中任何t列的支撑的并集不包含任何其他列的支撑。原则上,人们希望拥有这样一种矩阵,其行数最少为M。在本文中,我们考虑了有缺陷的项目是随机的并且遵循简单概率分布的情况。特别地,我们考虑以下情况:1)每个项目可以独立地以tN概率出现缺陷; 2)每个t组项目可以以统一的概率出现缺陷。在这两种情况下,我们的目的都是设计一个测试矩阵,以高概率成功识别出一组缺陷。这两种模型之前都已在文献中进行过研究,并且已知在两种情况下,Θ(tlogN)测试既必要又足够(通过随机编码)。我们的主要重点是针对上述情况的测试矩阵的明确确定性构造。构造测试矩阵的最流行方法之一是依靠恒定权重的纠错码及其最小距离。尤其是,众所周知,代码会导致带有O(t2logN)行的测试矩阵,这些行标识出任何t个缺陷。我们超越了最小距离分析,并将恒定权重代码的平均距离与结果测试矩阵的参数联系起来。确实,我们显示了距离,即矩阵列的成对属性如何转换为列的(t + 1)明智属性。在我们宽松的要求下,我们表明在第一种情况和第二种情况下,使用显式恒定权重代码(例如基于代数几何代码),我们可以实现等于O(t(log2N / logt))的许多测试。虽然与最佳测试次数仅相差(logN / logt)倍,但这是从确定性构造中可以获得的最佳参数集,而我们的主要贡献在于将组测试属性与平均值和恒定重量代码的最小距离。

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