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Published in:
18-12-2017
Analog to information convertor using cascaded transform and Gaussian random matrix
Published in: Cluster Computing | Special Issue 5/2019
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Abstract
In a communication system, there are different challenges to be faced in transmitting and receiving information. One of the major challenges is to use the bandwidth effectively. A recently developed technique known as compressive sensing provides a solution for using the bandwidth effectively by transmitting the samples of a compressed analog signal. For compressive sampling, the analog has to be converted into sparse, for which an apt transform must be used. Next, the analog must be sampled using a basis function. This paper proposes a novel analog to information converter where a cascaded transform is used to convert the image signal into sparse and where the sparse signal is compressed using a Gaussian random matrix. For signal recovery, an orthogonal matching pursuit is used. The performance of the proposed algorithm was measured both qualitatively and quantitatively, and the results demonstrated that the proposed algorithm is effective with all types of images.