Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Signal Reconstruction from Nonuniformly Spaced Samples Using Evolutionary Slepian Transform-Based POCS | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 367317 12 pages doi 2010 367317 Research Article Signal Reconstruction from Nonuniformly Spaced Samples Using Evolutionary Slepian Transform-Based POCS Jinsung Oh 1 Seda Senay 2 and Luis F. Chaparro EURASIP Member 2 1 Department of Electrical Engineering Halla University Wonju Gangwon 220-712 Republic of Korea 2 Department of Electrical and Computer Engineering University of Pittsburgh 348 Benedum Hall Pittsburgh PA 15261 USA Correspondence should be addressed to Luis F. Chaparro chaparro@ Received 29 January 2010 Accepted 15 June 2010 Academic Editor Aydin Akan Copyright 2010 Jinsung Oh et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. We consider the reconstruction of signals from nonuniformly spaced samples using a projection onto convex sets POCSs implemented with the evolutionary time-frequency transform. Signals of practical interest have finite time support and are nearly band-limited and as such can be better represented by Slepian functions than by sinc functions. The evolutionary spectral theory provides a time-frequency representation of nonstationary signals and for deterministic signals the kernel of the evolutionary representation can be derived from a Slepian projection of the signal. The representation of low pass and band pass signals is thus efficiently done by means of the Slepian functions. Assuming the given nonuniformly spaced samples are from a signal satisfying the finite time support and the essential band-limitedness conditions with a known center frequency imposing time and frequency limitations in the evolutionary transformation permit us to reconstruct the signal iteratively. Restricting the signal to a known finite time and frequency support a closed
