3 research outputs found
Practical High-Throughput, Non-Adaptive and Noise-Robust SARS-CoV-2 Testing
We propose a compressed sensing-based testing approach with a practical
measurement design and a tuning-free and noise-robust algorithm for detecting
infected persons. Compressed sensing results can be used to provably detect a
small number of infected persons among a possibly large number of people. There
are several advantages of this method compared to classical group testing.
Firstly, it is non-adaptive and thus possibly faster to perform than adaptive
methods which is crucial in exponentially growing pandemic phases. Secondly,
due to nonnegativity of measurements and an appropriate noise model, the
compressed sensing problem can be solved with the non-negative least absolute
deviation regression (NNLAD) algorithm. This convex tuning-free program
requires the same number of tests as current state of the art group testing
methods. Empirically it performs significantly better than theoretically
guaranteed, and thus the high-throughput, reducing the number of tests to a
fraction compared to other methods. Further, numerical evidence suggests that
our method can correct sparsely occurring errors.Comment: 8 Pages, 1 Figur
New construction of error-tolerant pooling designs
Abstract We present a new class of error-tolerant pooling designs by constructing d z −disjunct matrices associated with subspaces of a finite vector space