Affiliation:
1. Department of CSE, K.S. School of Engineering and Management, Bengaluru, India
2. Department of ECE, K.S. School
of Engineering and Management, Bengaluru, India
3. Department of CSE, R.V. Institute of Technology and Management,
Bengaluru, India
Abstract
Introduction: A more modern, extremely applicable method for signal acquisition is compression
sensing. It permits effective data sampling at a rate that is significantly lower than what the
Nyquist theorem suggests. Compressive sensing has a number of benefits, including a muchreduced
demand for sensory devices, a smaller memory storage need, a greater data transfer rate,
and significantly lower power usage. Compressive sensing has been employed in a variety of applications
because of all these benefits. Neuro-signal acquisition is a domain in which compressive
sensing has applications in the medical industry.
Methods: The novel methods discussed in this article are FFT-based CoSaMP (FFTCoSaMP),
DCT-based CoSaMP(DCTCoSaMP) and DWT-based CoSaMP (DWTCoSaMP) based on sparse
signal sequences / dictionaries by means of Transform Techniques, where CoSaMP stands for Compressive
Sampling Matching Pursuit with respect to Objective Quality Assessment Algorithms like
PSNR, SSIM and RMSE, where CoSaMP stands for Compressive Sampling Matching Pursuit.
Results: DWTCoSaMP is giving the PSNR values of 40.26 db, for DCTCoSaMP and FFTCoSaMP,
PSNR is 36.76 db and 34.76 db. For DWTCoSaMP, SSIM value is 0.8164, and for DCTCoSaMP
and FTCoSaMP, SSIM 0.719 and 0.5625 respectively. Finally, for DWTCoSaMP, RMSE value is
0.442, and for DCTCoSaMP and FFTCoSaMP, SSIM 0.44 and 0.4425, respectively.
Conclusion: Among Compressed sampling techniques DWTCoSaMP, DCTCoSaMP and FFTCoSaMP
discussed in this paper, DWTCoSaMP reveals the best results.
Publisher
Bentham Science Publishers Ltd.
Subject
Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials