Abstract / truncated to 115 words (read the full abstract)

Compressed Sensing (CS) is a widely used technique for efficient signal acquisition, in which a very small number of (possibly noisy) linear measurements of an unknown signal vector are taken via multiplication with a designed ‘sensing matrix’ in an application-specific manner, and later recovered by exploiting the sparsity of the signal vector in some known orthonormal basis and some special properties of the sensing matrix which allow for such recovery. We study three new applications of CS, each of which poses a unique challenge in a different aspect of it, and propose novel techniques to solve them, advancing the field of CS. Each application involves a unique combination of realistic assumptions on the measurement noise ... toggle 11 keywords

compressed sensing – group testing – COVID-19 – RT-PCR – graph signal processing – efficient deep learning – image moderation – compressive image recovery – eigenvector perturbation – image processing – steiner triple systems

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