Abstract
Compressive sensing is a new signal processing method which shows that a sparse signal can be constructed using fewer measurements than normal reconstruction methods. Rather taking all Nyquist samples of a sparse signal in any base the signal can be reconstructed correctly by taking small number of linear projections. In compressive sensing an important relation between measurement number and signal length and sparsity level is used as M=K(logN). This relation is examined separately in noiseless and noisy data. It is determined by simulations that this relation is valid for sparse enough signals and a new relation has been developed for more general cases.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
