International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Feb. 2024
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 132
Paper Published : 1541
No. of Authors : 4000
  Journal Paper

Paper Title :
Compressed Sensing Using Deterministic Measurement Matrix In WSN

Author :Sneha R.Walke, Prachi Jaini

Article Citation :Sneha R.Walke ,Prachi Jaini , (2014 ) " Compressed Sensing Using Deterministic Measurement Matrix In WSN " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 60-63, Volume-2,Issue-5

Abstract : Abstract: Compressive sensing is a sampling method which provides a new approach to efficient signal compression and recovery by exploiting the fact that a sparse signal can be suitably reconstructed from very few measurements. One of the most concerns in compressive sensing is the construction of the sensing matrices. While random sensing matrices have been widely studied, only a few deterministic sensing matrices have been considered. Originated as a technique for finding sparse solutions to underdetermined linear systems, compressed sensing (CS) has now found widespread applications in both Signal processing and Communication communities, ranging from data compression, data acquisition, inverse Problems, and channel coding. An essential idea of CS is to explore the fact that most natural phenomena are Sparse or compressible in some appropriate basis. By acquiring a relatively small number of samples in the “sparse” domain, the signal of interest can be reconstructed with high accuracy through well-developed optimization procedures. These matrices are highly desirable on structure which allows fast implementation with reduced storage requirements. In this paper, a survey of deterministic sensing matrices for compressive sensing is presented. Some recent results on construction of the deterministic sensing matrices are discussed.

Type : Research paper

Published : Volume-2,Issue-5


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