Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
Tuyển tập các báo cáo nghiên cứu khoa học ngành toán học được đăng trên tạp chí toán học quốc tế đề tài: Near optimal bound of orthogonal matching pursuit using restricted isometric constant | EURASIP Journal on Advances in Signal Processing SpringerOpen0 This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text HTML versions will be made available soon. Near optimal bound of orthogonal matching pursuit using restricted isometric constant EURASIP Journal on Advances in Signal Processing 2012 2012 8 doi 10.1186 1687-6180-2012-8 Jian Wang jwang@ipl.korea.ac.kr Seokbeop Kwon sbkwon@ipl.korea.ac.kr Byonghyo Shim bShim@korea.ac.kr ISSN 1687-6180 Article type Research Submission date 15 July 2011 Acceptance date 13 January 2012 Publication date 13 January 2012 Article URL http asp.eurasipiournals.com content 2012 1 8 This peer-reviewed article was published immediately upon acceptance. It can be downloaded printed and distributed freely for any purposes see copyright notice below . For information about publishing your research in EURASIP Journal on Advances in Signal Processing go to http asp.eurasipjournals.com authors instructions For information about other SpringerOpen publications go to http www.springeropen.com 2012 Wang et al. licensee Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Near optimal bound of orthogonal matching pursuit using restricted isometric constant Jian Wang Seokbeop Kwon and Byonghyo Shim School of Information and Communication Korea University Seoul 136-713 Korea Corresponding author bshim@korea.ac.kr Email addresses JW jwang@isl.korea.ac.kr SK sbkwon@isl.korea.ac.kr Abstract As a paradigm for reconstructing sparse signals using a set of under sampled measurements compressed sensing has received much attention in recent years. In identifying the sufficient condition under which the perfect recovery of sparse signals is ensured a property of the sensing .