By Topic

Rank-adaptive signal processing (RASP) a subspace approach to biological signal analysis .I. Principles

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Semnani, R.J. ; Dept. of Electr. Eng., Texas Univ., Austin, TX, USA ; Womack, B.F.

In many biomedical signal processing problems, the signal of interest is corrupted by noise and interference from other sources. A method to recover the signal is to decompose the data space into orthogonal subspaces through singular-value decomposition (SVD). Because of the conservation of energy in the time and SVD domains, these subspaces correspond to the various signal and noise components contained in the data. To filter the noise, the data is projected onto the desired signal subspace by simply setting the noise singular values in the singular value spectrum of the data to zero. The purpose of this paper is to describe the theoretical basis for the subspace approach, an alternative method of signal estimation in the presence of additive noise and interference. We describe the principles of a rank adaptive signal processing (RASP) approach to biomedical signal processing.

Published in:

Signals, Systems, and Computers, 1999. Conference Record of the Thirty-Third Asilomar Conference on  (Volume:1 )

Date of Conference:

24-27 Oct. 1999