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Peer Reviewed Article

Vol. 5 (2020)

Emerging Trends in Compressive Sensing for Efficient Signal Acquisition and Reconstruction

Published
2020-02-15

Abstract

This paper explores new compressive sensing (CS) directions for effective signal reconstruction and acquisition to clarify this novel framework's benefits, drawbacks, and consequences. The study aims to investigate the latest advancements in computer science algorithms, the incorporation of machine learning methods, adaptive sampling approaches, and their applications in diverse fields. Using a secondary data-based review technique, the study methodically reviews the literature from various sources, including research articles, review papers, and conference proceedings. Key conclusions from the survey highlight the versatility of computer science (CS) in applications related to medical imaging, remote sensing, wireless communications, and the Internet of Things (IoT). Additionally, CS may be integrated with machine learning to improve reconstruction accuracy and computational efficiency. However, issues like hardware implementation difficulties, noise resilience, and computational complexity still need to be addressed. The significance of policy implications underscores the need to tackle these obstacles via technological advancements, legislative modifications, and stakeholder partnerships to fully actualize the promise of computer science in molding the course of signal processing and data analysis in the future.

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